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1

Mixed-Integer Nonlinear Optimization - Optimization Online

Nov 22, 2012 ... sponse to a cyber attack (Goldberg et al., 2012; Altunay et al., 2011), wireless bandwidth allo- ...... be interpreted as an l1 trust region around the incumbent. In the case ..... MILANO is a MATLAB-based solver for convex MINLPs. ...... Optimization of 3D plasmonic crystal structures for refractive index sensing.

2012-12-02

2

A coupled gradient network approach for static and temporal mixed-integer optimization.

Utilizes the ideas of artificial neural networks to propose new solution methods for a class of constrained mixed-integer optimization problems. These new solution methods are more suitable to parallel implementation than the usual sequential methods of mathematical programming. Another attractive feature of the proposed approach is that some global search mechanisms may be easily incorporated into the computation, producing results which are more globally optimal. To formulate the solution method proposed in this paper, a penalty function approach is used to define a coupled gradient-type network with an appropriate architecture, energy function and dynamics such that high-quality solutions may be obtained upon convergence of the dynamics. Finally, it is shown how the coupled gradient net may be extended to handle temporal mixed-integer optimization problems, and simulations are presented which demonstrate the effectiveness of the approach. PMID:18263456

Watta, P B; Hassoun, M H

1996-01-01

3

NASA Astrophysics Data System (ADS)

Many applications of smart grid can be formulated as constrained optimization problems. Because of the discrete controls involved in power systems, these problems are essentially mixed-integer nonlinear programs. In this paper, we review the Trust-Tech-based methodology for solving mixed-integer nonlinear optimization. Specifically, we have developed a two-stage Trust-Tech-based methodology to systematically compute all the local optimal solutions for constrained mixed-integer nonlinear programming (MINLP) problems. In the first stage, for a given MINLP problem this methodology starts with the construction of a new, continuous, unconstrained problem through relaxation and the penalty function method. A corresponding dynamical system is then constructed to search for a set of local optimal solutions for the unconstrained problem. In the second stage, a reduced constrained NLP is defined for each local optimal solution by determining and fixing the values of integral variables of the MINLP problem. The Trust-Tech-based method is used to compute a set of local optimal solutions for these reduced NLP problems, from which the optimal solution of the original MINLP problem is determined. A numerical simulation of several testing problems is provided to illustrate the effectiveness of our proposed method.

Wang, Bin; Chiang, Hsiao-Dong

4

Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach

NASA Astrophysics Data System (ADS)

Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

5

Derivative-free Methods for Mixed-Integer Constrained Optimization ...

lem concerning the optimal design of an industrial electric motor. This allows to ..... the objective function, thus possibly improving the efficiency of the overall scheme. ...... speed, limited gross weight and extended speed range. Continuous

2014-03-04

6

Optimizing Constrained Mixed-Integer Nonlinear Programming Problems Using Nature Selection

Many practical engineering optimization problems involving real and integer\\/discrete design variables have been drawing much more attention from researchers. In this paper, an effective adaptive real-parameter simulated annealing genetic algorithm (ARSAGA) was proposed, applied to cope with constrained mixed-integer nonlinear programming problems. The performances of this proposed algorithm, including reliability and convergence speed are demonstrated by examples. It is noted

Rong-Song He

2009-01-01

7

Mixed integer programming approach to optimal short-term unit commitment for hydropower systems

Unit commitment problem is a complex decision-making process which involves the scheduling of generators over a set of time\\u000a periods to satisfy system load demand, water demand, system reliability, operational, and security constraints. Mathematically,\\u000a this is a nonlinear, nonconvex, high dimensional, and large-scale optimization problem over mixed integer variables. Additionally,\\u000a for a short-term unit commitment problem such as hourly or

Jaeeung Yi

1998-01-01

8

Integer tree-based search and mixed-integer optimal control of distribution chains

The use of integer tree-based search and mixed-integer programming is investigated for the purpose of control of multi-item multi-echelon distribution chains. A discrete-time model is considered to describe the dynamics of a generic distribution chain. The decisions on the amounts of goods to transfer are made by referring to a performance index that accounts for transportation, storage, and backlog costs

A. Alessandri; M. Gaggero; F. Tonelli

2011-01-01

9

Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 seconds, which confirms earlier results. We propose an iterative procedure for QP that allows to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iter...

Gorissen, Bram L; Hoffmann, Aswin L

2014-01-01

10

A Mixed-Integer Optimization Framework for De Novo Peptide Identification

A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum. PMID:19412358

DiMaggio, Peter A.

2009-01-01

11

NASA Astrophysics Data System (ADS)

Conventional treatment planning for interstitial prostate brachytherapy is generally a `trial and error' process in which improved treatment plans are generated by iteratively changing, via expert judgement, the configuration of sources within the target volume in order to achieve a satisfactory dose distribution. We have utilized linear mixed-integer programming (MIP) and the branch-and-bound method, a deterministic search algorithm, to generate treatment plans. The rapidity of dose falloff from an interstitial radioactive source requires fine sampling of the space in which dose is calculated. This leads to a large and complex model that is difficult to solve as a single 3D problem. We have therefore implemented an iterative sequential approach that optimizes pseudo-independent 2D slices to achieve a fine-grid 3D solution. Using our approach, treatment plans can be generated in 20-45 min on a 200 MHz processor. A comparison of our approach with the manual `trial and error' approach shows that the optimized plans are generally superior. The dose to the urethra and rectum is usually maintained below harmful levels without sacrificing target coverage. In the event that the dose to the urethra is undesirably high, we present a refined optimization approach that lowers urethra dose without significant loss in target coverage. An analysis of the sensitivity of the optimized plans to seed misplacement during the implantation process is also presented that indicates remarkable stability of the dose distribution in comparison with manual treatment plans.

D'Souza, W. D.; Meyer, R. R.; Thomadsen, B. R.; Ferris, M. C.

2001-02-01

12

NASA Astrophysics Data System (ADS)

Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of the existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 s, which confirms earlier results. We propose an iterative procedure for QP that allows us to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iterative procedure is comparable in speed to the LP model and produces better plans than the non-iterative QP. We formulate a new dose-volume-based model that maximizes V100% while satisfying pre-set DVH criteria. This model optimizes both catheter positions and dwell times within a few minutes depending on prostate volume and number of catheters, optimizes dwell times within 35 s and gives better DVH statistics than dose-based models. The solutions suggest that the correlation between the objective value and the clinical plan quality is weak in the existing dose-based models.

Gorissen, Bram L.; den Hertog, Dick; Hoffmann, Aswin L.

2013-02-01

13

NASA Astrophysics Data System (ADS)

We present a novel model-based mixed-integer optimal control method to automatically identify the strength and timing of critical external stimuli leading to the transient annihilation of limit-cycle oscillators. Biochemical oscillators of this type play a central role in regulating cellular rhythms. Their specific manipulation is a promising perspective to control biological functions by drugs and tailored treatment strategies. We demonstrate our new optimal control approach in an application to a biochemical model for oscillatory calcium signal transduction.

Lebiedz, D.; Sager, S.; Bock, H. G.; Lebiedz, P.

2005-09-01

14

NASA Astrophysics Data System (ADS)

We discuss the planning of transportation by trucks over a multi-day period. Each truck collects loads from suppliers and delivers them to assembly plants or a truck terminal. By exploiting the truck terminal as a temporal storage, we aim to increase the load ratio of each truck and to minimize the lead time for transportation. In this paper, we show a mixed integer programming model which represents each product explicitly, and discuss the decomposition of the problem into a problem of delivery and storage, and a problem of vehicle routing. Based on this model, we propose a relax-and-fix type heuristic in which decision variables are fixed one by one by mathematical programming techniques such as branch-and-bound methods.

Sakakibara, Kazutoshi; Tian, Yajie; Nishikawa, Ikuko

15

Optimal Control of Multi-Vehicle-Systems Under Communication Constraints Using Mixed-Integer

is outlined. It enables to consider the indi- vidual vehicle's motion dynamics with individual, physical cooperative control of individual motion dynamics for multi-vehicle systems is investigated. In this context of Hybrid Dynamical Multi-Vehicle Sys- tems Many geometrical motivated approaches haven proposed

Stryk, Oskar von

16

Facility location optimization is very important for many retail industries, such as banking network, chain stores, and so on. Maximal covering location problem (MCLP) is one of the well-known models for these facility location optimization problems, which has earned extensive research interests. However, various practical requirements limit the application of the traditional formulation of MCLP, and the NP-hard characteristic makes

Li Xia; Yanjia Zhao; Ming Xie; Jinyan Shao; Jin Dong

2008-01-01

17

Optimizing well-stimulation treatment size using mixed integer linear programming

This thesis presents a model for optimum sizing of well stimulation treatments. The optimization scheme is based on a combination of a well stimulation type selection and treatment size, therefore determining the optimum type of stimulation...

Picon Aranguren, Oscar

2012-06-07

18

Efficient upper and lower bounds for global mixed-integer optimal ...

Probably for the first time, we present a complete analysis of the optimal solution of such a ... relaxation order, the obtained series of upper and lower bounds converge for the ... predictive control of vehicles is studied, e.g., in [10]. A hybrid .... outer convexification as discussed in [31,35] for the general case, is here equivalent.

2013-11-07

19

Based on the research of the management of container multimodal hub inside and outside, analyses facility location problem of container multimodal hub status in our country in this paper. And a Mixed Integer Programming model base on Minimum cost of transport is built under the consideration of industry distribution, the import and export quantities of the container, the location of

Lin Jianxin; Song Rui

2010-01-01

20

Online trajectory planning for UAVs using mixed integer linear programming

This thesis presents a improved path planner using mixed-integer linear programming (MILP) to solve a receding horizon optimization problem for unmanned aerial vehicles (UAV's). Using MILP, hard constraints for obstacle ...

Culligan, Kieran Forbes

2006-01-01

21

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

Cheung, Kar-Ming; Lee, Charles H.

2012-01-01

22

Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO(2)) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To ease the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time. PMID:17395362

Ko, Andi Setiady; Chang, Ni-Bin

2008-07-01

23

Mixed Integer Programming and Heuristic Scheduling for Space Communication

NASA Technical Reports Server (NTRS)

Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.

Lee, Charles H.; Cheung, Kar-Ming

2013-01-01

24

Optimal design applications are often modeled by using categorical variables to express discrete design decisions, such as material types. A disadvantage of using categorical variables is the lack of continuous relaxations, which precludes the use of modern integer programming techniques. We show how to express categorical variables with standard integer modeling techniques, and we illustrate this approach on a load-bearing thermal insulation system. The system consists of a number of insulators of different materials and intercepts that minimize the heat flow from a hot surface to a cold surface. Our new model allows us to employ black-box modeling languages and solvers and illustrates the interplay between integer and nonlinear modeling techniques. We present numerical experience that illustrates the advantage of the standard integer model.

Abhishek, K.; Leyffer, S.; Linderoth, J. T.; Mathematics and Computer Science; Lehigh Univ.

2010-06-01

25

CBLIB 2014: A benchmark library for conic mixed-integer and ...

Mar 7, 2014 ... field of conic mixed-integer and continuous optimization, which is already ... public test sets mixing cone types and allowing integer variables, but ... improvements in reliability and performance in optimization software. The.

2014-03-07

26

COVER AND PACK INEQUALITIES FOR (MIXED) INTEGER ...

fundamental substructures of MIPs contributed to this success substantially by strength- ... Since every constraint of a mixed integer program defines a knapsack ... Department of Industrial Engineering and Operations Research 4135 ...

2003-07-12

27

SCIP - a framework to integrate Constraint and Mixed Integer Programming

Constraint Programs and Mixed Integer Programs are closely re- lated optimization problems originating from dieren t scientic areas. Today's state-of-the-art algorithms of both elds have several strategies in common, in particular the branch-and-bound process to recursively divide the problem into smaller subproblems. On the other hand, the main techniques to process each subproblem are dieren t, and it was observed

Tobias Achterberg

28

WATER SUPPLY SIMULATION BY A MIXED INTEGER NETWORK FLOW IMBEDDED LINEAR PROGRAMMING MODEL

For water supply simulation, we developed a sophisticated multi-period mixed integer network flow imbedded linear programming model to evaluate the water supply capability of a hydrologic basin with multiple reservoirs in real world setting. This optimization model is a large scale mixed integer programming model that consists of 3,920 integer variables and 68,658 by 132,384 node-arc incidence matrix for 28

Sheung-Kown Kim; Youngjoon Park; JaeHee Kim

29

Mixed Integer Programming for Finding Tensegrity Structures

Mixed Integer Programming for Finding Tensegrity Structures Shintaro Ehara Yoshihiro Kanno University of Tokyo (Japan) November 4, 2009 #12;tensegrity -- definition MIP for Finding Tensegrities CODE at most one strut s topology () #12;tensegrity -- definition MIP for Finding Tensegrities CODE 2009 Â 2

Kanno, Yoshihiro

30

A Lagrangean based Branch-and-Cut algorithm for global optimization of nonconvex Mixed commercial global optimization solvers that are based on branch and bound. Key words. Global optimization guarantee global optimality for problems with special structures, and usually involve some form of a branch

Grossmann, Ignacio E.

31

MIXED INTEGER LINEAR PROGRAMMING FORMULATION ...

Jul 22, 2014 ... motivating example that allow us to precisely define the idea of a MIP formulation ..... and formulations whose LP relaxation project precisely to conv(S) yield the best ...... Surveys in Operations Research and Management Science, 17 (2012), pp. ... [52] Fair Isaac Corporation, FICO Xpress Optimization Suite.

2014-07-22

32

Strengthening Gomory Mixed-Integer Cuts: A Computational Study

Gomory mixed-integer cuts are an important ingredient in state-of- the-art software for solving mixed-integer linear programs. In particu- lar, much attention has been paid to the strengthening of these cuts. In this paper, we give an overview of existing approaches for improv- ing the performance of Gomory mixed-integer cuts. More precisely, we consider k-cuts, combined Gomory mixed-integer cuts, reduce-and-split cuts,

Franz Wesselmann

2009-01-01

33

Optimization Online - Solving mixed integer nonlinear programming ...

One of the key reasons is that material of different grades becomes mixed on a ... be exploited to yield effective algorithms that incorporate post-mining material ... several extended formulations of the OPMPSP+S and discuss the strength of the

Andreas Bley

34

Transmission planning for Indian power grid: a mixed integer programming approachp

Transmission planning for Indian power grid: a mixed integer programming approachp Rajeev utilization of generation and transmission capacity. The model also examines optimal transmission expansion and the Indian power system considered. SpeciÂ®c emphasis is on spatial transmission expansion plan

Dragoti-Ã?ela, Eranda

35

A mixed-integer linear program for the real-time railway traffic management problem

A mixed-integer linear program for the real-time railway traffic management problem: Quantification.pellegrini, gregory.marliere, joaquin.rodriguez}@ifsttar.fr Mots-clÃ©s : real-time railway traffic management problem-optimal scheduling strategy, but dispatchers in charge of managing traffic in a control area typically do not dispose

Paris-Sud XI, UniversitÃ© de

36

Solid Waste Management System Analysis by Multiobjective Mixed Integer Programming Model

The conflict between economic optimization and environmental protection has received wide attention in recent research programs for solid waste management system planning. The purpose of this analysis is to apply multiobjective mixed integer programming techniques for reasoning the potential conflict between environmental and economic goals and for evaluating sustainable strategies for waste management in a metropolitan region. The information incorporated

Ni-Bin Chang; S. F. Wang

1996-01-01

37

On the group problem for mixed integer programming

A theory is developed for a group problem arising from mixed integer programming. This theory gives descriptions of functions\\u000a on the unit hypercube from which cutting planes can be constructed for any mixed integer program. Methods for generating such\\u000a functions are given.

Ellis L. Johnson

38

A DC Programming Approach for Mixed-Integer Linear Programs

In this paper, we propose a new efficient algorithm for globally solving a class of Mixed Integer Program (MIP). If the objective\\u000a function is linear with both continuous variables and integer variables, then the problem is called a Mixed Integer Linear\\u000a Program (MILP). Researches on MILP are important in both theoretical and practical aspects. Our approach for solving a general

Yi-shuai Niu; Pham Dinh Tao

2008-01-01

39

Integer and mixed-integer programming models: General properties

It is well known that mixed-integer formulations can be used tomodel important classes of nonconvex functions, such as fixed-charge functions and linear economy-of-scale cost functions. The purpose of this paper is to formulate a rigorous definition of a mixed-integer model of a given function and to study the properties of the functions that can be so modelled. An interesting byproduct

R. R. Meyer

1975-01-01

40

Optimization models for the dynamic facility location and allocation problem

The design of logistic distribution systems is one of the most critical and strategic issues in industrial facility management. The aim of this study is to develop and apply innovative mixed integer programming optimization models to design and manage dynamic (i.e. multi-period) multi-stage and multi-commodity location allocation problems (LAP). LAP belong to the NP-hard complexity class of decision problems, and

Riccardo Manzini; Elisa Gebennini

2008-01-01

41

Whole-Farm Evaluation of No-Till Profitability in Rice Production using Mixed Integer Programming

Rice production in Arkansas usually involves intensive tillage. No-till rice has been studied, but the focus has been limited to impacts on yields and per acre returns. This study uses mixed integer programming to model optimal machinery selection and evaluate whole-farm profitability of no-till management, for rice-soybean farms. Results indicate that lower machinery ownership expenses combined with lower fuel and

K. Bradley Watkins; Jason L. Hill; Merle M. Anders; Tony E. Windham

2006-01-01

42

This paper addresses the problem of inventory management of a refinery that imports several types of crude oil which are delivered by different vessels. This problem involves optimal operation of crude oil unloading, its transfer from storage tanks to charging tanks, and the charging schedule for each crude oil distillation unit. A mixed-integer optimization model is developed which relies on

Heeman Lee; J. M. Pinto; I. E. Grossmann

1996-01-01

43

Mixed integer programming formulation for hybrid flow shop scheduling problem

This paper addresses a complex hybrid flow shop (HFS) scheduling problem confronted in a real industrial environment in which a manufacturing firm provides electroplating service to the electronic and semiconductor industries. A mixed integer programming (MIP) formulation was developed to represent the scheduling problem. Data was taken from the manufacturing firm to test the MIP to in terms of obtaining

Mohamed K. Omar; S. C. Teo; Y. Suppiah

2010-01-01

44

Lookahead Branching for Mixed Integer Programming Wasu Glankwamdee

Lookahead Branching for Mixed Integer Programming Wasu Glankwamdee Jeff Linderoth Department of a lookahead branching method for the selec- tion of branching variable in branch-and-bound method for mixed of the current branching decision on the bounds of the child nodes two levels deeper than the current node, can

Linderoth, Jeffrey T.

45

Average shadow price in a mixed integer linear programming problem

Recently a new concept of shadow price, the average shadow price, based on the average and not on the marginal contribution of a resource, has been developed for pure Integer Linear Programming problems. In this paper we prove that average shadow prices can be used in a Mixed Integer Linear Programming problems and that some of its properties are analogous

Alejandro Crema

1995-01-01

46

Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches

Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches Michael C. Ferris Robert R. Meyer Warren D'Souza October 2002 Abstract Radiation therapy is extensively used diagnosed with cancer in the U.S will undergo treatment with radiation therapy. This form of therapy has

Ferris, Michael C.

47

Parametric Mixed Integer Programming: An Application to Solid Waste Management

A method is developed for carrying out parametric analysis on a mixed integer linear program (MILP) as either objective function coefficients or right-hand-side values of the constraints are varied continuously. The method involves solving MILPs at point values of the parameters of variation and joining the results by LP parametric analysis. The procedure for parametric analysis on the objective function

Larry Jenkins

1982-01-01

48

SOLVING MIXED INTEGER BILINEAR PROBLEMS USING MILP ...

Key words. bilinear problems, McCormick envelopes, binary expansion, ...... However, we found no performance gain with this approach. ...... [22] R. Karuppiah and I.E. Grossmann, Global optimization for the synthesis of integrated water.

2013-01-29

49

A chaotic firefly algorithm applied to reliability-redundancy optimization

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer non- linear programming. On the other hand, a broad class of meta- heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA

Leandro dos Santos Coelho; Diego Luis de Andrade Bernert; Viviana Cocco Mariani

2011-01-01

50

Lift-and-Project Cuts for Mixed Integer Convex Programs

\\u000a This paper addresses the problem of generating cuts for mixed integer nonlinear programs where the objective is linear and\\u000a the relations between the decision variables are described by convex functions defining a convex feasible region. We propose\\u000a a new method for strengthening the continuous relaxations of such problems using cutting planes. Our method can be seen as\\u000a a practical implementation

Pierre Bonami

51

Safe bounds in linear and mixed-integer linear programming

Current mixed-integer linear programming solvers are based on linear programming routines that use ?oating-point arithmetic. Occasionally, this leads to wrong solutions, even for problems where all coe-cients and all solution components are small integers. An example is given where many state-of-the-art MILP solvers fail. It is then shown how, using directed rounding and interval arithmetic, cheap pre- and postprocessing of

Arnold Neumaier; Oleg Shcherbina

2004-01-01

52

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony search (HS) algorithm has emerged. HS was conceptualized using

Leandro dos Santos Coelho; Diego Luis de Andrade Bernert; Viviana Cocco Mariani

2009-01-01

53

NASA Technical Reports Server (NTRS)

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Laird, Philip

1992-01-01

54

Reduce-and-Split Cuts: Improving the Performance of Mixed-Integer Gomory Cuts

Mixed-integer Gomory cuts have become an integral part of state-of-the-art software for solving mixed-integer linear programming problems. Therefore, improvements in the performance of these cutting planes can be of great practical value. In this paper, we present a simple and fast heuristic for improving the coefficients on the continuous variables in the mixed-integer Gomory cuts. This is motivated by the

Kent Andersen; Gérard Cornuéjols; Yanjun Li

2005-01-01

55

The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems. This research project added to the understanding of this area by making a number of fundamental advances. First, the work demonstrated many novel, strong, tractable relaxations designed to deal with non-convexities arising in mathematical formulation. Second, the research implemented the ideas in software that is available to the public. Finally, the work demonstrated the importance of these ideas on practical applications and disseminated the work through scholarly journals, survey publications, and conference presentations.

Linderoth, Jeff T. [University of Wisconsin-Madison] [University of Wisconsin-Madison; Luedtke, James R. [University of Wisconsin-Madison] [University of Wisconsin-Madison

2013-05-30

56

Reduce-and-split cuts: Improving the performance of mixed integer Gomory cuts 1

part of state-of-the-art software for solving mixed integer linear programming problems. Therefore integer programming, cutting plane, split cut, mixed integer Gomory cut, reduce-and-split cut 1 integer linear programs using cutting planes dates back to the work of Gomory [18] in the late fifties

Cornuejols, Gerard P.

57

The Integer Approximation Error in Mixed-Integer Optimal Control

lems (MIOCPs) in ordinary differential equations (ODE) of the following form. We want to ..... Theorem 2 Let x(·) and y(·) be solutions of the initial value problems ..... means that we can find a solution to the convexified problem that is affine.

2010-08-18

58

Global Optimization of Mixed-Integer Quadratically- Constrained ...

Nov 14, 2011 ... studies are presented for point packing problems, standard and generalized ... plications including heat integration networks, separation systems, ... of maximizing profit subject to feedstock availability, intermediate storage capacity, ...... and-bound tree takes a significant fraction of the total allotted CPU limit.

2011-11-14

59

Planning Investments in Water Resources by Mixed-Integer Programming: The Vardar-Axios River Basin

A mixed integer programming model for planning water resources investments is presented. The model is a sequencing model applied to the Vardar-Axios river basin in Yugoslavia and Greece. The structure of the model is ...

Elliot, Dorothy P.

60

CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY

CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY programming (MINLP) formulation to achieve mini- mum-cost designs for reinforced concrete (RC) structures for concrete, steel reinforcing bars, and formwork according to typical contractor methods. Restrictions

61

An Interval Mixed-Integer Semi-Infinite Programming Method for Municipal Solid Waste Management

This study proposed an interval mixed-integer semi-infinite programming (IMISIP) method for solid waste management under uncertainty. The uncertainty can be expressed as various constants, intervals, and functional intervals. The method is mainly based on the previous efforts on interval mixed-integer linear programming (IMILP) and semi-infinite programming. The method is applied to a solid-waste management system to illustrate its effectiveness in

Li He; Guo H. Huang; Guangming Zeng; Hongwei Lu; John Koupal; Fred Minassian; Hannah Murray; Mani Natarajan; Fenfen Zhu; Masaki Takaoka; Kazuyuki Oshita; Shinsuke Morisawa; Hiroshi Tsuno; Yoshinori Kitajima; Wan-Fu Chiang; Hung-Yuan Fang; Chao-Hsiung Wu; Chang-Jun Huang; Ching-Yuan Chang; Yu-Min Chang; Ching-Liang Chen; Anders Nielsen; Lars Nielsen; Anders Feilberg; Knud Christensen; Yu-Yin Liu; Ta-Chang Lin; Ying-Jan Wang; Wei-Lun Ho; Janet Yanowitz; Robert McCormick; Lei Yu; Shichen Jia; Qinyi Shi; Tsang-Jung Chang; Hong-Ming Kao; Yu-Ting Wu; Wei-Hua Huang; Patrick Goodman; David Rich; Ariana Zeka; Luke Clancy; Douglas Dockery; Thomas Lavery; Christopher Rogers; Ralph Baumgardner; Kevin Mishoe; Wei-Chin Chen; Hsun-Yu Lin; Chung-Shin Yuan; Chung-Hsuang Hung; Guo Huang

2009-01-01

62

A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem

NASA Technical Reports Server (NTRS)

Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad

2010-01-01

63

A Mixed Integer Linear Program for Airport Departure Scheduling

NASA Technical Reports Server (NTRS)

Aircraft departing from an airport are subject to numerous constraints while scheduling departure times. These constraints include wake-separation constraints for successive departures, miles-in-trail separation for aircraft bound for the same departure fixes, and time-window or prioritization constraints for individual flights. Besides these, emissions as well as increased fuel consumption due to inefficient scheduling need to be included. Addressing all the above constraints in a single framework while allowing for resequencing of the aircraft using runway queues is critical to the implementation of the Next Generation Air Transport System (NextGen) concepts. Prior work on airport departure scheduling has addressed some of the above. However, existing methods use pre-determined runway queues, and schedule aircraft from these departure queues. The source of such pre-determined queues is not explicit, and could potentially be a subjective controller input. Determining runway queues and scheduling within the same framework would potentially result in better scheduling. This paper presents a mixed integer linear program (MILP) for the departure-scheduling problem. The program takes as input the incoming sequence of aircraft for departure from a runway, along with their earliest departure times and an optional prioritization scheme based on time-window of departure for each aircraft. The program then assigns these aircraft to the available departure queues and schedules departure times, explicitly considering wake separation and departure fix restrictions to minimize total delay for all aircraft. The approach is generalized and can be used in a variety of situations, and allows for aircraft prioritization based on operational as well as environmental considerations. We present the MILP in the paper, along with benefits over the first-come-first-serve (FCFS) scheme for numerous randomized problems based on real-world settings. The MILP results in substantially reduced delays as compared to FCFS, and the magnitude of the savings depends on the queue and departure fix structure. The MILP assumes deterministic aircraft arrival times at the runway queues. However, due to taxi time uncertainty, aircraft might arrive either earlier or later than these deterministic times. Thus, to incorporate this uncertainty, we present a method for using the MILP with "overlap discounted rolling planning horizon". The approach is based on valuing near-term decision results more than future ones. We develop a model of taxitime uncertainty based on real-world data, and then compare the baseline FCFS delays with delays using the above MILP in a simple rolling-horizon method and in the overlap discounted scheme.

Gupta, Gautam; Jung, Yoon Chul

2009-01-01

64

Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming.

We present a novel method for estimating tree-structured covariance matrices directly from observed continuous data. Specifically, we estimate a covariance matrix from observations of p continuous random variables encoding a stochastic process over a tree with p leaves. A representation of these classes of matrices as linear combinations of rank-one matrices indicating object partitions is used to formulate estimation as instances of well-studied numerical optimization problems.In particular, our estimates are based on projection, where the covariance estimate is the nearest tree-structured covariance matrix to an observed sample covariance matrix. The problem is posed as a linear or quadratic mixed-integer program (MIP) where a setting of the integer variables in the MIP specifies a set of tree topologies of the structured covariance matrix. We solve these problems to optimality using efficient and robust existing MIP solvers.We present a case study in phylogenetic analysis of gene expression and a simulation study comparing our method to distance-based tree estimating procedures. PMID:22081761

Bravo, Héctor Corrada; Wright, Stephen; Eng, Kevin H; Keles, Sündüz; Wahba, Grace

2009-01-01

65

A Two-Stage Stochastic Mixed-Integer Programming Approach to the Smart House Scheduling Problem

NASA Astrophysics Data System (ADS)

A “Smart House” is a highly energy-optimized house equipped with photovoltaic systems (PV systems), electric battery systems, fuel cell cogeneration systems (FC systems), electric vehicles (EVs) and so on. Smart houses are attracting much attention recently thanks to their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC system and an EV. We formulate the problem as a mixed integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real-life demands and PV power generation to show the effectiveness of our method.

Ozoe, Shunsuke; Tanaka, Yoichi; Fukushima, Masao

66

Path Planning Via CPLEX Optimization

This paper presents an optimized solution of finding time-optimal trajectories for autonomous systems. These systems are subject to avoidance requirements, which include avoidance of collisions with other systems and obstacles, either static or dynamic. The necessary constraints for avoidance are added to a time-optimizing linear program by including a binary variable in the optimization. The resulting problem is a mixed-integer

Taoridi A. Ademoye; A. Davaril; Charles C. Castello; Sharon Fan; Jeffrey Fan

2008-01-01

67

In this study, an interval-parameter two-stage mixed integer linear programming (ITMILP) model is developed for supporting long-term planning of waste management activities in the City of Regina. In the ITMILP, both two-stage stochastic programming and interval linear programming are introduced into a general mixed integer linear programming framework. Uncertainties expressed as not only probability density functions but also discrete intervals can be reflected. The model can help tackle the dynamic, interactive and uncertain characteristics of the solid waste management system in the City, and can address issues concerning plans for cost-effective waste diversion and landfill prolongation. Three scenarios are considered based on different waste management policies. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment or justification of the existing waste flow allocation patterns, the long-term capacity planning of the City's waste management system, and the formulation of local policies and regulations regarding waste generation and management. PMID:16678336

Li, Y P; Huang, G H

2006-11-01

68

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context. PMID:19854040

Guo, P; Huang, G H

2010-03-01

69

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context.

Guo, P., E-mail: guoping@iseis.or [College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing 100083 (China); Huang, G.H., E-mail: gordon.huang@uregina.c [Environmental Systems Engineering Program, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); College of Urban and Environmental Sciences, Peking University, Beijing, 100871 (China)

2010-03-15

70

A mixed integer program was structured to identify the least cost combinations of recycling and treatment alternatives that can be used to control the liquid, solid, and gas waste streams produced from a 750-megawatt coal fired steam electric power plant. The model compared the ability of methods of liquid stream recycle and waste discharge treatment to meet given air and

M. F. Torpy; A. B. Bishop; R. Narayanan

1978-01-01

71

On the Facets of Mixed Integer Programs with Two Integer Variables and Two Constraints

In this paper we consider an infinite relaxation of the mixed integer linear program with two integer variables and two constraints, and we give a complete characterization of its facets. We then derive an analogous characterization of the facets of the underlying finite integer program.

Gérard Cornuéjols; François Margot

2008-01-01

72

General Mixed Integer Programming: Computational Issues for Branch-and-Cut Algorithms

Abstract. In this paper we survey the basic features of state-of-the-art branch-and-cut algorithms for the solution of general mixed integer pro- gramming problems. In particular we focus on preprocessing techniques, branch-and-bound issues and cutting plane generation.

Alexander Martin

2001-01-01

73

Concrete Structure Design Using Mixed-Integer Nonlinear Programming with Complementarity Constraints

Concrete Structure Design Using Mixed-Integer Nonlinear Programming with Complementarity) formulation to achieve minimum-cost designs for reinforced concrete (RC) structures that satisfy building code requirements. The objective function includes material and labor costs for concrete, steel reinforcing bars

74

A Lifted Linear Programming Branch-and-Bound Algorithm for Mixed Integer Conic Quadratic Programs

A Lifted Linear Programming Branch-and-Bound Algorithm for Mixed Integer Conic Quadratic Programs develops a linear programming based branch-and-bound algorithm for mixed in- teger conic quadratic programs. The algorithm is based on a higher dimensional or lifted polyhedral relaxation of conic quadratic constraints

Ahmed, Shabbir

75

We extend past research on the economic lot scheduling problem to address some of the limitations of the earlier work. In particular we develop mixed integer programming formulations with the assumption of a production precedence sequence. When evaluated over a variety of problems from the literature, it is clear that finding a schedule that minimizes costs is not trivial. However,

David L. Cooke; Thomas R. Rohleder; Edward A. Silver

2004-01-01

76

Recent work on solving job-shop scheduling problems with earliness and tardi- ness costs has led to several hybrid algorithms that are more eective than any one method alone. In particular, these hybrid methods have been found to be more ef- fective than pure mixed integer programming (MIP) approaches. This paper takes a fresh look at MIP formulations in light of

Emilie Danna; Edward Rothberg; Claude Le Pape

77

Hydra-MIP: Automated Algorithm Configuration and Selection for Mixed Integer Programming

Hydra-MIP: Automated Algorithm Configuration and Selection for Mixed Integer Programming Lin Xu performance, and hence it is useful to select from a portfolio of different configurations. HYDRA is a recent innovations, HYDRA can achieve strong performance for MIP. First, we describe a new algorithm selec- tion

Hutter, Frank

78

Amending and enhancing electoral laws through mixed integer programming: the case of Italy

Amending and enhancing electoral laws through mixed integer programming: the case of Italy Aline Pennisi 1 , Federica Ricca 1 , Paolo Serafini 2 , Bruno Simeone 3 1 Electoral Systems expert, Rome, Italy 2 Dept. of Mathematics and Computer Science, University of Udine, Italy 3 Dept. of Statistics, La

Serafini, Paolo

79

Efficient lifting methods for unstructured mixed integer programs with multiple constraints

In this thesis, we introduce efficient lifting methods to generate strong cutting planes for unstructured mixed integer programs (MIPs) with multiple constraints. Our results include improved sequential lifting methods and novel sequence independent lifting methods using multidimensional superadditive lifting functions. ^ First we investigate sequential lifting for general 0–1 MIPs. We introduce a new scheme to obtain strong bounds on

Bo Zeng

2007-01-01

80

In recent years, an operation planning of a district heating and cooling (DHC) plant has been arousing interest as a result of development of cooling load or heat demand prediction methods for district heating and cooling systems. In this paper, we formulate an operation planning of a district heating and cooling plant as a mixed integer linear programming problem. Since

Masatoshi Sakawa; Kosuke Kato; Satoshi Ushiro; Mare Inaoka

2001-01-01

81

Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ?10 days to ?5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering. PMID:21949695

Kim, Joonhoon; Reed, Jennifer L; Maravelias, Christos T

2011-01-01

82

Approximating the Stability Region for Binary Mixed-Integer Programs

Nov 13, 2007 ... In our model, where only the objective coefficients of the x variables are un- ..... and 2 GB RAM, and using CPLEX 9.0 as the optimization engine. ..... the pure binary program is an important special case, we plan to investigate.

2007-11-13

83

A mixed integer programming model for remanufacturing in reverse logistics environment

Recently, there has been a growing interest in reverse logistics due to environmental deterioration. Firms incorporate reverse\\u000a flow to their systems for such reasons as ecological and economic factors, government regulations and social responsibilities.\\u000a In this paper a new mixed integer mathematical model for a remanufacturing system, which includes both forward and reverse\\u000a flows, is proposed and illustrated on a

Neslihan Özgün Demirel; Hadi Gökçen

2008-01-01

84

Development of refinery scheduling system using mixed integer programming and expert system

This paper presents a hybrid refinery scheduling system combining mathematical programming model and expert system. Mixed-integer\\u000a linear programming models for crude oil movement between units are merged into the expert system that is for qualitative issues\\u000a concerning crude vessel unloading operations. The target problem ranging from the crude unloading to the crude charging to\\u000a distillation towers is decomposed into several

Jin-Kwang Bok; Heeman Lee; Jay Woo Chang

2002-01-01

85

In this study, an interval-parameter two-stage mixed integer linear programming (ITMILP) model is developed for supporting long-term planning of waste management activities in the City of Regina. In the ITMILP, both two-stage stochastic programming and interval linear programming are introduced into a general mixed integer linear programming framework. Uncertainties expressed as not only probability density functions but also discrete intervals

Y. P. Li; G. H. Huang

2006-01-01

86

A common way to produce a convex relaxation of a Mixed Integer Quadratically Constrained Program (MIQCP) is to lift the problem\\u000a into a higher-dimensional space by introducing variables Y\\u000a \\u000a ij\\u000a to represent each of the products x\\u000a \\u000a i\\u000a \\u000a x\\u000a \\u000a j\\u000a of variables appearing in a quadratic form. One advantage of such extended relaxations is that they can be efficiently strengthened

Anureet Saxena; Pierre Bonami; Jon Lee

87

Using a Mixed Integer Quadratic Programming Solver for the Unconstrained Quadratic 0-1 Problem

In this paper, we consider problem (P) of minimizing a quadratic function q(x)=x\\u000a \\u000a t\\u000a \\u000a Qx+c\\u000a \\u000a t\\u000a \\u000a x of binary variables. Our main idea is to use the recent Mixed Integer Quadratic Programming (MIQP) solvers. But, for this,\\u000a we have to first convexify the objective function q(x). A classical trick is to raise up the diagonal entries of Q by a

Alain Billionnet; Sourour Elloumi

2007-01-01

88

Advances in mixed-integer programming methods for chemical production scheduling.

The goal of this paper is to critically review advances in the area of chemical production scheduling over the past three decades and then present two recently proposed solution methods that have led to dramatic computational enhancements. First, we present a general framework and problem classification and discuss modeling and solution methods with an emphasis on mixed-integer programming (MIP) techniques. Second, we present two solution methods: (a) a constraint propagation algorithm that allows us to compute parameters that are then used to tighten MIP scheduling models and (b) a reformulation that introduces new variables, thus leading to effective branching. We also present computational results and an example illustrating how these methods are implemented, as well as the resulting enhancements. We close with a discussion of open research challenges and future research directions. PMID:24910915

Velez, Sara; Maravelias, Christos T

2014-01-01

89

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with

P. Guo; G. H. Huang

2010-01-01

90

This paper presents a branch and bound (B&B) algorithm for the 0-1 mixed integer knapsack problem with linear multiple choice constraints. The formulation arose in an application to transportation management for allocating funds to highway improvements. Several model properties are developed and utilized to design a B&B solution algorithm. The algorithm solves at each node of the B&B tree a

George Kozanidis; Emanuel Melachrinoudis

2004-01-01

91

Biomass electricity plant allocation through non-linear modeling and mixed integer optimization.

?? Electricity generation from the combustion of biomass feedstocks provides low-carbon energy that is not as geographically constricted as other renewable technologies. This dissertation uses… (more)

Smith, Robert Kennedy

2012-01-01

92

Supply Chain Planning Matrix (Meyr et al., 2002) SugarcaneSugarcane WoodWood Switch grass Switch grass Wood waste Wood waste SugarsSugars CornCorn Corn GrainCorn Grain Corn Stover Corn Stover Hydrolysis Zeolite upgrading Phenols &BTX Phenols &BTX AromaticsAromatics Fast pyrolysis Fast pyrolysis

Grossmann, Ignacio E.

93

Optimization by record dynamics

NASA Astrophysics Data System (ADS)

Large dynamical changes in thermalizing glassy systems are triggered by trajectories crossing record sized barriers, a behavior revealing the presence of a hierarchical structure in configuration space. The observation is here turned into a novel local search optimization algorithm dubbed record dynamics optimization, or RDO. RDO uses the Metropolis rule to accept or reject candidate solutions depending on the value of a parameter akin to the temperature and minimizes the cost function of the problem at hand through cycles where its ‘temperature’ is raised and subsequently decreased in order to expediently generate record high (and low) values of the cost function. Below, RDO is introduced and then tested by searching for the ground state of the Edwards-Anderson spin-glass model, in two and three spatial dimensions. A popular and highly efficient optimization algorithm, parallel tempering (PT), is applied to the same problem as a benchmark. RDO and PT turn out to produce solutions of similar quality for similar numerical effort, but RDO is simpler to program and additionally yields geometrical information on the system’s configuration space which is of interest in many applications. In particular, the effectiveness of RDO strongly indicates the presence of the above mentioned hierarchically organized configuration space, with metastable regions indexed by the cost (or energy) of the transition states connecting them.

Barettin, Daniele; Sibani, Paolo

2014-03-01

94

-integer optimization models that exhibit strong continuous relaxations. 1. Introduction Mixed-integer optimization in the development of Mixed-integer Linear/ Nonlinear Programming (MILP/MINLP) models in Process Systems Engineering tends to be MILP models[6][9][24][28] although gradually there is also an increasing trend to MINLP

Grossmann, Ignacio E.

95

. The resulting regression problem is solved numerically via standard branch-and-bound techniques. The performance of the method is tested on simulated data generated by a simple model of Escherichia coli nutrient stress network identification via kinetic mod- elling. In the context of Boolean networks, it was observed

Ferrari-Trecate, Giancarlo

96

Optimization techniques in molecular structure and function elucidation

This paper discusses recent optimization approaches to the protein side-chain prediction problem, protein structural alignment, and molecular structure determination from X-ray diffraction measurements. The machinery employed to solve these problems has included algorithms from linear programming, dynamic programming, combinatorial optimization, and mixed-integer nonlinear programming. Many of these problems are purely continuous in nature. Yet, to this date, they have been approached mostly via combinatorial optimization algorithms that are applied to discrete approximations. The main purpose of the paper is to offer an introduction and motivate further systems approaches to these problems. PMID:20160866

Sahinidis, Nikolaos V.

2009-01-01

97

Adaptive critics for dynamic optimization

A novel action-dependent adaptive critic design (ACD) is developed for dynamic optimization. The proposed combination of a particle swarm optimization-based actor and a neural network critic is demonstrated through dynamic sleep scheduling of wireless sensor motes for wildlife monitoring. The objective of the sleep scheduler is to dynamically adapt the sleep duration to node’s battery capacity and movement pattern of

Raghavendra V. Kulkarni; Ganesh Kumar Venayagamoorthy

2010-01-01

98

Integer optimization methods for machine learning

In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks in ...

Chang, Allison An

2012-01-01

99

Optimization Online - Integer Programming Submissions - 2012

A conic representation of the convex hull of disjunctive sets and conic cuts for integer ... Two-stage Models and Algorithms for Optimizing Infrastructure Design and ... Solving mixed integer nonlinear programming problems for mine production ...

100

Dynamic Bundle Methods - Optimization Online

dynamic bundle method giving a positive answer for its primal-dual convergence properties, and, under ..... Bundle information gives at each iteration a model of the dual function f, namely the ...... Validation of subgradient optimization. Math.

2007-03-15

101

Conceptual Design of Biorefineries Through the Synthesis of Optimal Chemical-reaction Pathways

on developing new pathways while optimizing existing ones. Here, potential chemicals are added to create a superstructure, then an algorithm is run to enumerate every feasible reaction stoichiometry through a mixed integer linear program (MILP). An optimal...

Pennaz, Eric James

2011-10-21

102

Cost minimization for coal conversion pollution control: a mixed integer programming model

The model compared methods of liquid stream recycle and waste discharge treatment to meet given air and water quality standards and was then used to study the effects on the optimal solution of changes in capital, operation and maintenance, and energy and water costs. The effects on optimum system design of changes in particulate and sulfur oxide emission standards and

Torpy

1978-01-01

103

Background The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. Results This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. Conclusion The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON. PMID:22574924

2012-01-01

104

Optimized supply routing at Dell under non-stationary demand

This thesis describes the design and implementation of an optimization model to manage inventory at Dell's American factories. Specifically, the model is a mixed integer program which makes routing decisions on incoming ...

Foreman, John William

2008-01-01

105

Optimal dynamic detection of explosives

The detection of explosives is a notoriously difficult problem, especially at stand-off distances, due to their (generally) low vapor pressure, environmental and matrix interferences, and packaging. We are exploring optimal dynamic detection to exploit the best capabilities of recent advances in laser technology and recent discoveries in optimal shaping of laser pulses for control of molecular processes to significantly enhance the standoff detection of explosives. The core of the ODD-Ex technique is the introduction of optimally shaped laser pulses to simultaneously enhance sensitivity of explosives signatures while reducing the influence of noise and the signals from background interferents in the field (increase selectivity). These goals are being addressed by operating in an optimal nonlinear fashion, typically with a single shaped laser pulse inherently containing within it coherently locked control and probe sub-pulses. With sufficient bandwidth, the technique is capable of intrinsically providing orthogonal broad spectral information for data fusion, all from a single optimal pulse.

Moore, David Steven [Los Alamos National Laboratory; Mcgrane, Shawn D [Los Alamos National Laboratory; Greenfield, Margo T [Los Alamos National Laboratory; Scharff, R J [Los Alamos National Laboratory; Rabitz, Herschel A [PRINCETON UNIV; Roslund, J [PRINCETON UNIV

2009-01-01

106

In this thesis we consider different joint production and transportation problems. We first study the simplest two-level problem, the uncapacitated two-level production-in-series lot-sizing problem (2L-S\\/LS-U). We give a new polynomial dynamic programming algorithm and a new compact extended formulation for the problem and for an extension with sales. Some computational tests are performed comparing several reformulations on a NP-Hard problem

Rafael Augusto De Melo

2011-01-01

107

New numerical methods for open-loop and feedback solutions to dynamic optimization problems

NASA Astrophysics Data System (ADS)

The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.

Ghosh, Pradipto

108

Mojo: A Dynamic Optimization System

Dynamic optimization systems have the flexibility to adapt program execution to changing scenarios and differing hardware configurations. Previous work has shown that this flexibility can be exploited for sizeable performance improvement. Yet, the work to date has been chiefly targeted towards running the SPEC benchmarks on scientific workstations. We contend that this technology is also important to the desktop computing

Wen-Ke Chen; Sorin Lerner; Ronnie Chaiken; David M. Gillies

2000-01-01

109

Time O®set Optimization in Digital Broadcasting¤

[18] a local search algorithm and a mixed integer programming model are presented ... dynamic row generation can be performed by a polynomial time oracle (x4). ..... validation of SC and a computational comparison between SC and ILPM is ...

110

DOI 10.1007/s11081-013-9226-6 A mixed-integer nonlinear program for the optimal

in alternative sources of energy for commercial building applications due to their potential to supply on generators or to take advantage of time periods in which utility prices are low. Our research considers

111

associated with volatile utility pricing and potentially high system capital costs. Energy technology and boilers), and/or thermal energy storage (e.g., hot water). For some markets, volatile utility pricing heat and power Fuel cells Building energy a b s t r a c t The distributed generation (DG) of combined

112

Final Report-Optimization Under Uncertainty and Nonconvexity: Algorithms and Software

The goal of this research was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems.

Jeff Linderoth

2008-10-10

113

A DYNAMIC GRADIENT APPROACH TO PARETO OPTIMIZATION ...

Jun 5, 2014 ... ative games; sparse optimization; inverse problems; gradient methods. ... whence the decentralized features of this dynamic. ...... point of the dynamics (see Figures 1 and 2), making the process realistic in engineering and human .... multi-

2014-06-06

114

Optimizing Adaptive Control Allocation With Actuator Dynamics

In this work we address the optimizing control allocation problem for an over-actuated nonlinear time-varying system with actuator dynamic where parameters affine in the actuator and effector model may be assumed unknown. Instead of optimizing the control allocation at each time instant, a dynamic approach is considered by constructing actuator reference update-laws that represent an asymptotically optimal allocation search. By

Tor Arne Johansen

2008-01-01

115

Optimizing adaptive control allocation with actuator dynamics

In this work we address the optimizing control allocation problem for an over-actuated nonlinear time-varying system with actuator dynamic where parameters affine in the actuator and effector model may be assumed unknown. Instead of optimizing the control allocation at each time instant, a dynamic approach is considered by constructing actuator reference update-laws that represent an asymptotically optimal allocation search. By

J. Tjonnas; T. A. Johansen

2007-01-01

116

Dynamic Reactive Power Control of Isolated Power Systems

Doubly Fed Induction Generator DG Distributed Generator MILP Mixed Integer Linear Programming MIQP Mixed Integer Quadratic Programming MLD Mixed Logical Dynamics MPC Model Predictive Control PWA...-12 Schematic diagram of conventional control of DFIG wind generator ....... 81 Figure 3-13 Schematic diagram of conventional control of photovoltaic source .......... 85 Figure 3-14 Reactive response of a wind DG and the estimated reactive power...

Falahi, Milad

2012-10-03

117

TRACKING CODE DEVELOPMENT FOR BEAM DYNAMICS OPTIMIZATION

Dynamic aperture (DA) optimization with direct particle tracking is a straight forward approach when the computing power is permitted. It can have various realistic errors included and is more close than theoretical estimations. In this approach, a fast and parallel tracking code could be very helpful. In this presentation, we describe an implementation of storage ring particle tracking code TESLA for beam dynamics optimization. It supports MPI based parallel computing and is robust as DA calculation engine. This code has been used in the NSLS-II dynamics optimizations and obtained promising performance.

Yang, L.

2011-03-28

118

Two Characterizations of Optimality in Dynamic Programming

It holds in great generality that a plan is optimal for a dynamic programming problem, if and only if it is 'thrifty' and 'equalizing.' An alternative characterization of an optimal plan, that applies in many economic models, is that the plan must satisfy an appropriate Euler equation and a transversality condition. Here we explore the connections between these two characterizations.

Karatzas, Ioannis, E-mail: ik@math.columbia.ed [Columbia University, Department of Mathematics (United States); Sudderth, William D., E-mail: bill@stat.umn.ed [University of Minnesota, School of Statistics (United States)

2010-06-15

119

Dynamic optimization of artificial lighting in greenhouses

A principle for dynamic optimization of artificial lighting in greenhouses is presented, where the optimization criterion is maximization of the term

E. Heuvelink; H. Challa

1989-01-01

120

Real-time path-planning using mixed-integer linear programming and global cost-to-go maps

With the advance in the fields of computer science, control and optimization, it is now possible to build aerial vehicles which do not need pilots. An important capability for such autonomous vehicles is to be able to ...

Toupet, Olivier

2006-01-01

121

Optimization of dynamic processes in boiler aggregate

NASA Astrophysics Data System (ADS)

A technique is developed for optimal control of dynamic processes in boiler aggregate based on the nonlinear mathematical programming and its program implementation is conducted. A comparison is presented for the regulation of dynamic process of the loading variation of the TP-81 boiler on the basis of the given technique and while using the conventional proportional-integral-differential regulators.

Kler, A. M.; Zharkov, P. V.

2007-09-01

122

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

Laird, Phil

1992-01-01

123

Dynamic optimization and adaptive controller design

NASA Astrophysics Data System (ADS)

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

Inamdar, S. R.

2010-10-01

124

Optimal process design often requires the solution of mixed integer non-linear programming problems. Optimization procedures must be robust and efficient if they are to be incorporated in automated design systems. For heat integrated separation process design, a natural hybrid evolutionary\\/local search method with these properties is possible. The method is based on the use of local search methods for the

E. S. Fraga

2003-01-01

125

Dynamic Optimization for Optimal Control of Water Distribution Systems

range of scheduling problems. Keywords: Dynamic Programming, Optimal Control, Level based control, Reinforcement Learning 1. INTRODUCTION In this paper we consider the design of intelligent control policies. Our objective is to design a pump-scheduler that controls pumps at a booster station to serve

Ertin, Emre

126

Design Optimization Using Dynamic Evaluation

We describe a search strategy that may be use ful for a class of design problems by develop ing an example from cancer radiation treat ment planning. This application problem in volves typical features of design problems such as constraints, optimality, a large search space with continuously varying parameters as well as discrete (non-numeric) parameters. There is no known method

Witold Paluszynski; Ira Kalet

1989-01-01

127

Optimal facility layout design

The facility layout problem (FLP) is a fundamental optimization problem encountered in many manufacturing and service organizations. Montreuil introduced a mixed integer programming (MIP) model for FLP that has been used as the basis for several rounding heuristics. However, no further attempt has been made to solve this MIP optimally. In fact, though this MIP only has 2n(n?1) 0–1 variables,

Russell D. Meller; Venkat Narayanan; Pamela H. Vance

1998-01-01

128

NASA Astrophysics Data System (ADS)

The blending procedure, selecting from various kinds of zinc-bearing materials containing different contents of components and determining their mixing ratios, is the first step in the hydrometallurgical zinc process. Whenever a setup takes place to change the selection of materials or their ratios in the blending process, the setup itself and the adjustments required in the subsequent processes incur cost, time, and effort. Reducing the number of setups by keeping the blending ratios of the selected materials for the longest possible period is critical for consistent operation and processing and, thus, for assuring the quality of the final zinc product. In this article, we formulate a mixed integer programming model for the blending schedule that minimizes the number of setups over a planning horizon, given the constraints on the contents of components and the daily schedules of material supply and zinc production. Also, we propose an efficient heuristic, which provides a solution of good quality within reasonable time bounds. The applicability and efficiency of the model along with its heuristic are verified through simulation experiments as well as the model’s application to a real zinc refinery.

Kim, Seong-In; Han, Junghee; Lee, Youngho; Shim, Bo-Kyung; Hameed, Ibrahim A.

2008-12-01

129

Role of controllability in optimizing quantum dynamics

This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.

Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel [Department of Automation, Tsinghua University, Beijing, 100084, China and Center for Quantum Information Science and Technology, TNList, Beijing, 100084 (China); Department of Chemistry and Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90025 (United States); Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States)

2011-06-15

130

Policy optimization for dynamic power management

Dynamic power management schemes (also called policies) can be used to control the power consumption levels of electronic systems, by setting their components in different states, each characterized by a performance level and a power consumption. In this paper, we describe power-managed systems using a finite-state, stochastic model. Furthermore, we show that the fundamental problem of finding an optimal policy

Giuseppe A. Paleologo; Luca Benini; Alessandro Bogliolo; Giovanni De Micheli

1998-01-01

131

Optimal 3D layout of industrial facilities

Facilities layout is concerned with the spatial arrangement of a set of departments or equipment items. This is an important stage at the design level, which often results in a complex problem due to the high number of decisions involved. A Mixed Integer Linear Programming (MILP) formulation, for the optimal layout of facilities within a two-dimensional (2D) continuous space has

Ana Paula Barbosa-Póvoa; Ricardo Mateus; Augusto Q. Novais

2002-01-01

132

Optimal multiperiod operational planning for utility systems

In this paper, the operational planning problem for utility systems is formulated as a mixed-integer linear program (MILP). For multiperiod operation with piecewise constant varying demands for utilities, the optimal choice of units for each period is determined. The objective function accounts for both the operating costs for each period and changeover costs for startup\\/shutdown of units each between periods

Ramaswamy R. Iyer; Ignacio E. Grossmann

1997-01-01

133

Optimal design of an electrodialysis brackish water desalination plant

This paper considers the optimal design and operation of electrodialysis (ED) desalination plants. In general an ED plant aims to produce potable water from a high salinity source, like brackish water or high salinity water. The system is modelled mathematically as mixed-integer non-linear programming (MINLP) optimization problem, determining the number of desalination stages, the membrane area, the total required energy

Panagiotis Tsiakis; Lazaros G. Papageorgiou

2005-01-01

134

Branch-and-Cut Algorithms for Combinatorial Optimization and Their Implementation in ABACUS

Branch-and-cut (-and-price) algorithms belong to the most successful techniques for solving mixed integer linear programs\\u000a and combinatorial optimization problems to optimality (or, at least, with certified quality). In this unit, we concentrate\\u000a on sequential branch-and-cut for hard combinatorial optimization problems, while branch-and-cut for general mixed integer\\u000a linear programming is treated in [? Martin] and parallel branch-and-cut is treated in [?

Matthias Elf; Carsten Gutwenger; Michael Jünger; Giovanni Rinaldi

2001-01-01

135

Sham Kakade Dynamic Mechanism Design: Optimality through Efficiency

Sham Kakade Title: Dynamic Mechanism Design: Optimality through Efficiency Abstract: We consider the problem of revenueoptimal dynamic mechanism design in settings where on the environment permit the design of revenue optimal mechanisms. We present the Virtual Index Mechanism which

Plotkin, Joshua B.

136

Optimal reactive\\/voltage control by an improved harmony search algorithm

This paper presents an improved harmony search algorithm for the optimal reactive\\/voltage control problem. Optimal reactive\\/voltage control is a mixed integer, nonlinear optimization problem which includes both continuous and discrete control variables. The proposed algorithm is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation

A. H. Khazali; A. Parizad; M. Kalantar

2010-01-01

137

IMPACT OF DYNAMIC VOLTAGE SCALING (DVS) ON CIRCUIT OPTIMIZATION

Circuit designers perform optimization procedures targeting speed and power during the design of a circuit. Gate sizing can be applied to optimize for speed, while Dual-VT and Dynamic Voltage Scaling (DVS) can be applied to optimize for leakage...

Esquit Hernandez, Carlos A.

2010-01-16

138

Design and Engineering of a Dynamic Binary Optimizer

In today's software, which increasingly utilizes dynamic class loading, shared libraries, and interconnected components, the power and reach of static compiler optimization is diminishing. An exciting new paradigm of transparent dynamic binary optimization is emerging, aimed at improving the performance of a program while it executes. Recently, several dynamic binary optimization systems have appeared in the literature. They all share

EVELYN DUESTERWALD

2005-01-01

139

Optimal reduction of flexible dynamic system

Dynamic system reduction is basic procedure in various problems of active control synthesis of flexible structures. In this paper is presented direct method for system reduction by explicit extraction of modes included in reduced model form. Criterion for optimal system discrete approximation in synthesis reduced dynamic model is also presented. Subjected method of system decomposition is discussed in relation to the Schur method of solving matrix algebraic Riccati equation as condition for system reduction. By using exposed method procedure of flexible system reduction in addition with corresponding example is presented. Shown procedure is powerful in problems of active control synthesis of flexible system vibrations.

Jankovic, J. [Univ. of Belgrade (Yugoslavia)

1994-12-31

140

Dynamics of Dengue epidemics using optimal control

We present an application of optimal control theory to Dengue epidemics. This epidemiologic disease is an important theme in tropical countries due to the growing number of infected individuals. The dynamic model is described by a set of nonlinear ordinary differential equations, that depend on the dynamic of the Dengue mosquito, the number of infected individuals, and the people's motivation to combat the mosquito. The cost functional depends not only on the costs of medical treatment of the infected people but also on the costs related to educational and sanitary campaigns. Two approaches to solve the problem are considered: one using optimal control theory, another one by discretizing first the problem and then solving it with nonlinear programming. The results obtained with OC-ODE and IPOPT solvers are given and discussed. We observe that with current computational tools it is easy to obtain, in an efficient way, better solutions to Dengue problems, leading to a decrease of infected mosquitoes and individ...

Rodrigues, Helena Sofia; Torres, Delfim F M

2010-01-01

141

DYNAMIC EMBEDDED OPTIMIZATION AND SHOOTING METHODS FOR POWER

Chapter 9 DYNAMIC EMBEDDED OPTIMIZATION AND SHOOTING METHODS FOR POWER SYSTEM PERFORMANCE@ieee.org Abstract Power system dynamic performance enhancement can often be formu- lated as a dynamic embedded optimization problem. The associated cost function quantifies performance and involves dynamically evolving

142

Dynamo: A Staged Compiler Architecture for Dynamic Program Optimization

SyntaxPossibleDynamic InputValue-Specific OptimizationsRegister AllocationCoarse SchedulingCode GenerationAMMA GPossibleDynamic InputPeephole OptimizationCode LayoutBranch PredictionAssemblyELTA DPossibleDynamic InputPeephole OptimizationCode LayoutBranch PredictionAssemblyELTA DPossibleDynamic InputDynamic InputDynamically OptimizingNative CodePSILON ECreate Specialized Code GeneratorsJava VMCodeLPHA AHigh-Level IRMid-Level IRLow-Level IRNative Code...

Mark Leone; R. Kent Dybvig Indiana

1997-01-01

143

A Probabilistic Approach to Optimal Robust Path Planning with Obstacles

such as Unmanned Air Vehicles (UAVs) has received a great deal of attention in recent years [1][2][3][4]. UAVs need-optimal trajectories for vehicles modeled as linear systems. The Mixed-Integer Linear Pro- gramming approach uses learning-and-query approach known as Probabilistic Roadmaps. These approaches do not, however, take

Williams, Brian C.

144

Conceptual modeling to optimize the haul and transfer of municipal solid waste

Two conceptual mixed integer linear optimization models were developed to optimize the haul and transfer of municipal solid waste (MSW) prior to landfilling. One model is based on minimizing time (h\\/d), whilst the second model is based on minimizing total cost (€\\/d). Both models aim to calculate the optimum pathway to haul MSW from source nodes (waste production nodes, such

D. P. Komilis

2008-01-01

145

The Automatic Formulating Method of the Optimal Operating Planning Problem for Energy Supply Systems

The problem of the optimal operating planning for energy supply system is formulated as mixed-integer linear programming (MILP), but, it is too complicated for most untrained operators with little experience to apply the method. This paper proposes an automatic evaluating method of the optimal operating planning for energy supply system in using simple data. The problem can be formulated only

Naohiko Suzuki; Takaharu Ueda; Koichi Sasakawa

2004-01-01

146

An new efficient evolutionary approach for dynamic optimization problems

To improve the efficiency of the currently known evolutionary algorithms for dynamic optimization problems, we have proposed a novel variable representation allows static evolutionary optimization approaches to be extended to efficiently explore global and better local optimal areas in dynamic fitness landscapes. It represents a single individual as three real-valued vectors (x,¿,r)¿ Rn ?? Rn ?? R2 in the evolutionary

Yong Liang

2009-01-01

147

Luo and Zhang: DSM and Optimization 1 Dynamic Spectrum Management

Luo and Zhang: DSM and Optimization 1 Dynamic Spectrum Management: Complexity, Duality and Zhang: DSM and Optimization 2 Dynamic Spectrum Management (DSM) Multiuser communication system: Â· K interferences. Shuzhong Zhang, The Chinese University of Hong Kong #12;Luo and Zhang: DSM and Optimization 3

Zhang, Shuzhong

148

Optimization of dynamic systems using collocation methods

NASA Astrophysics Data System (ADS)

The time-based simulation is an important tool for the engineer. Often a time-domain simulation is the most expedient to construct, the most capable of handling complex modeling issues, or the most understandable with an engineer's physical intuition. Aeroelastic systems, for example, are often most easily solved with a nonlinear time-based approach to allow the use of high fidelity models. Simulations of automatic flight control systems can also be easier to model in the time domain, especially when nonlinearities are present. Collocation is an optimization method for systems that incorporate a time-domain simulation. Instead of integrating the equations of motion for each design iteration, the optimizer iteratively solves the simulation as it finds the optimal design. This forms a smooth, well-posed, sparse optimization problem, transforming the numerical integration's sequential calculation into a set of constraints that can be evaluated in any order, or even in parallel. The collocation method used in this thesis has been improved from existing techniques in several ways, in particular with a very simple and computationally inexpensive method of applying dynamic constraints, such as damping, that are more traditionally calculated with linear models in the frequency domain. This thesis applies the collocation method to a range of aircraft design problems, from minimizing the weight of a wing with a flutter constraint, to gain-scheduling the stability augmentation system of a small-scale flight control testbed, to aeroservoelastic design of a large aircraft concept. Collocation methods have not been applied to aeroelastic simulations in the past, although the combination of nonlinear aerodynamic analyses with structural dynamics and stability constraints is well-suited to collocation. The results prove the collocation method's worth as a tool for aircraft design, particularly when applied to the multidisciplinary numerical models used today.

Holden, Michael Eric

149

Facility layout design and planning within construction sites are a common construction management problem and regarded as a complex combinatorial problem. To transport heavy materials, tower cranes are needed and should be well located to reduce operating costs and improve overall efficiency. Quadratic assignment problem (QAP), non-linear in nature, has been developed to simulate the material transportation procedure. Applying linear

C. Huang; C. K. Wong; C. M. Tam

2011-01-01

150

A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu. PMID:22788846

Baliban, Richard C.; DiMaggio, Peter A.; Plazas-Mayorca, Mariana D.; Garcia, Benjamin A.; Floudas, Christodoulos A.

2012-01-01

151

An Optimal Dynamic Threat Evaluation and Weapon Scheduling Technique

NASA Astrophysics Data System (ADS)

Real time scheduling problems demand high level of flexibility and robustness under complex dynamic scenarios. Threat Evaluation (TE) and Weapon Assignment (WA), together TEWA is one such complex dynamic system having optimal or near optimal utilization of scarce defensive resources of supreme priority. Several static solutions of TEWA have been proposed. This paper discusses an optimal dynamic multi-air threat evaluation and weapon allocation algorithm using a variant of Stable Marriage Algorithm (SMA). WA uses a new dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution. Testing part of this paper shows feasibility of this approach for a range of scenarios.

Naeem, H.; Masood, A.

152

at node . Electrical susceptance of transmission element . Number of open transmission elements. IIEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 23, NO. 3, AUGUST 2008 1469 Optimal Transmission Switching optimal dispatch of generation and transmission topology to meet load as a mixed integer program (MIP

Ferris, Michael C.

153

Ant Colony Optimization with Immigrants Schemes in Dynamic Environments

Ant Colony Optimization with Immigrants Schemes in Dynamic Environments Michalis Mavrovouniotis1 of the pop- ulation and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants immigrants schemes are applied to ant colony optimization (ACO) for the dynamic travelling salesman problem

Yang, Shengxiang

154

Optimal control of HIV/AIDS dynamic: Education and treatment

NASA Astrophysics Data System (ADS)

A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.

Sule, Amiru; Abdullah, Farah Aini

2014-07-01

155

Chaotic Dynamics in Optimal Monetary Policy

There is by now a large consensus in modern monetary policy. This consensus has been built upon a dynamic general equilibrium model of optimal monetary policy as developed by, e.g., Goodfriend and King (1997), Clarida et al. (1999), Svensson (1999) and Woodford (2003). In this paper we extend the standard optimal monetary policy model by introducing nonlinearity into the Phillips curve. Under the specific form of nonlinearity proposed in our paper (which allows for convexity and concavity and secures closed form solutions), we show that the introduction of a nonlinear Phillips curve into the structure of the standard model in a discrete time and deterministic framework produces radical changes to the major conclusions regarding stability and the efficiency of monetary policy. We emphasize the following main results: (i) instead of a unique fixed point we end up with multiple equilibria; (ii) instead of saddle--path stability, for different sets of parameter values we may have saddle stability, totally unstable equilibria and chaotic attractors; (iii) for certain degrees of convexity and/or concavity of the Phillips curve, where endogenous fluctuations arise, one is able to encounter various results that seem intuitively correct. Firstly, when the Central Bank pays attention essentially to inflation targeting, the inflation rate has a lower mean and is less volatile; secondly, when the degree of price stickiness is high, the inflation rate displays a larger mean and higher volatility (but this is sensitive to the values given to the parameters of the model); and thirdly, the higher the target value of the output gap chosen by the Central Bank, the higher is the inflation rate and its volatility.

Orlando Gomes; Vivaldo M. Mendes; Diana A. Mendes; J. Sousa Ramos

2006-07-28

156

Chaotic dynamics in optimal monetary policy

NASA Astrophysics Data System (ADS)

There is by now a large consensus in modern monetary policy. This consensus has been built upon a dynamic general equilibrium model of optimal monetary policy as developed by, e.g., Goodfriend and King [ NBER Macroeconomics Annual 1997 edited by B. Bernanke and J. Rotemberg (Cambridge, Mass.: MIT Press, 1997), pp. 231 282], Clarida et al. [J. Econ. Lit. 37, 1661 (1999)], Svensson [J. Mon. Econ. 43, 607 (1999)] and Woodford [ Interest and Prices: Foundations of a Theory of Monetary Policy (Princeton, New Jersey, Princeton University Press, 2003)]. In this paper we extend the standard optimal monetary policy model by introducing nonlinearity into the Phillips curve. Under the specific form of nonlinearity proposed in our paper (which allows for convexity and concavity and secures closed form solutions), we show that the introduction of a nonlinear Phillips curve into the structure of the standard model in a discrete time and deterministic framework produces radical changes to the major conclusions regarding stability and the efficiency of monetary policy. We emphasize the following main results: (i) instead of a unique fixed point we end up with multiple equilibria; (ii) instead of saddle-path stability, for different sets of parameter values we may have saddle stability, totally unstable equilibria and chaotic attractors; (iii) for certain degrees of convexity and/or concavity of the Phillips curve, where endogenous fluctuations arise, one is able to encounter various results that seem intuitively correct. Firstly, when the Central Bank pays attention essentially to inflation targeting, the inflation rate has a lower mean and is less volatile; secondly, when the degree of price stickiness is high, the inflation rate displays a larger mean and higher volatility (but this is sensitive to the values given to the parameters of the model); and thirdly, the higher the target value of the output gap chosen by the Central Bank, the higher is the inflation rate and its volatility.

Gomes, O.; Mendes, V. M.; Mendes, D. A.; Sousa Ramos, J.

2007-05-01

157

MULTI-OBJECTIVE OPTIMIZATION TECHNIQUES FOR DESIGNING AIRCRAFT ENGINE PROTECTION SYSTEMS

The purpose of engine protection systems is to serve as a device for preventing aircraft from malfunction. In this paper, we apply optimization techniques to resolve this problem for simultaneously obtaining maximal system reliability and system total cost. A multi -objective nonlinear mixed-integer programming model is developed for this problem. An efficient heuristic solution method is constructed for solving this

Chiun-Ming Liu; Jeng-Lung Li

158

Optimization of caviar and meat production from white sturgeon ( Acipenser transmontanus)

A multi-period mixed integer linear programming model was developed to optimize caviar and meat production from a white sturgeon production facility over a 51 year time horizon. The model is only moderately complex but is large with 10 102 equations and 17 905 variables. The model size allows for system stabilization and a minimization of end of model effects without

E. M. Wade; J. G. Fadel

1997-01-01

159

A model is proposed for optimizing the net benefits of removing multiple dams in U.S. watersheds of Lake Erie by quantifying impacts upon social, ecological, and economic objectives of importance to managers and stakeholders. Explicit consideration is given to the linkages between newly accessible tributary habitat and the lake's ecosystem. The model is a mixed integer linear program (MILP) that

Pearl Q. Zheng; Benjamin F. Hobbs; Joseph F. Koonce

2009-01-01

160

1 Optimal supply chain design and management over a multi-period horizon under demand uncertainty motors. Keywords: supply chain, demand uncertainty, inventory management, mixed integer non An optimization model is proposed to redesign the supply chain of spare part delivery under demand uncertainty

Grossmann, Ignacio E.

161

An event-driven optimization framework for dynamic vehicle routing

An event-driven optimization framework for dynamic vehicle routing Victor Pillac1,2 , Christelle Gu and practitioners in industry. In this work we focus on dynamic vehicle routing problems and present an event be adapted to tackle the dynamic vehicle routing problem with stochastic demands. Computational results show

Paris-Sud XI, UniversitÃ© de

162

A fast re-optimization approach for dynamic vehicle routing

A fast re-optimization approach for dynamic vehicle routing Victor P 1,2 , Christelle G ,1 Mines de Nantes France Abstract The present work deals with dynamic vehicle routing problems in which a controlled number of changes. Keywords: Dynamic vehicle routing, route consistency, bi-objective opti

Paris-Sud XI, UniversitÃ© de

163

Multi-objective optimization for deepwater dynamic umbilical installation analysis

NASA Astrophysics Data System (ADS)

We suggest a method of multi-objective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation while maintaining structural safety. The approximation model is constructed by the design of experiment (DOE) sampling and is utilized to solve the problem of time-consuming analyses. The non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. Intuitionist fuzzy set theory is applied for selecting the best compromise solution from Pareto set. The optimization results indicate this optimization strategy with approximation model and multiple attribute decision-making method is valid, and provide the optimal deployment method for deepwater dynamic umbilical buoyancy modules.

Yang, HeZhen; Wang, AiJun; Li, HuaJun

2012-08-01

164

Dynamic systems of regional economy management optimization

NASA Astrophysics Data System (ADS)

One of the most actual problems of the Russian economic life is a regional economic systems formation. The hierarchy of economic and branch priorities should follow from the general idea of an industrial policy. The matter is that the concept of an industrial policy is defined by the system of priorities mainly incorporated in it. The problem of priorities is not solved yet neither on federal, nor at a regional level. It is necessary to recognize, that a substantiation of this or that variant of priorities - objectively a challenge. Such substantiation can be received with the help of dynamic structural modeling and management technology. At formation of the regional industrial policy program the special attention is given to creation of modern type commercial structures. In regions there are headquarters and branches of many largest corporations, holdings and banks. Besides it, many regional enterprises already became inter-regional or even the transnational companies. In this connection an assistance of transformation of the industrial enterprises and their groups in vertically integrated companies and modern type holdings can become a prominent aspect of an industrial policy. Regional economic structures should be reconstructed gradually on the general model of the world class competitive companies. Assistance to creation of new corporational control systems, the organization of headquarters and the central services work - all this can be included into the sphere of regional administration industrial policy. The special attention should be turned on necessity of development of own system of the corporate structures, capable to provide to the region an independent participation in use of the natural resources and industrial-technological potential, at the stage of a regional industrial policy program formation. Transformation of the industrial enterprises and their groups into modern type vertically-integrated companies and holdings can become one of the major directions of an industrial policy of region. The situational-analytical centers (SAC) of regional administration The major component of SAC is dynamic modeling, analysis, forecasting and optimization systems, based on modern intellectual information technologies. Spheres of SAC are not only financial streams management and investments optimization, but also strategic forecasting functions, which provide an optimum choice, "aiming", search of optimum ways of regional development and corresponding investments. It is expedient to consider an opportunity of formation of the uniform organizational-methodical center of an industrial policy of region. This organization can be directly connected to the scheduled-analytical services of the largest economic structures, local authorities, the ministries and departments. Such "direct communication" is capable to provide an effective regional development strategic management. Anyway, the output on foreign markets demands concentration of resources and support of authorities. Offered measures are capable to provide a necessary coordination of efforts of a various level economic structures. For maintenance of a regional industrial policy an attraction of all newest methods of strategic planning and management is necessary. Their activity should be constructed on the basis of modern approaches of economic systems management, cause the essence of an industrial policy is finally reduced to an effective regional and corporate economic activities control centers formation. Opportunities of optimum regional economy planning and management as uniform system Approaches to planning regional economic systems can be different. We will consider some most effective methods of planning and control over a regional facilities condition. All of them are compact and evident, that allows to put them into the group of average complexity technologies. At the decision of problems of a regional resource management is rather perspective the so-called "topographical" approach, which is used by intellectual information tec

Trofimov, S.; Kudzh, S.

165

PLASMA Approximate Dynamic Programming finally cracks the locomotive optimization problem

PLASMA Â Approximate Dynamic Programming finally cracks the locomotive optimization problem schedules and new operating policies. PLASMA is currently running at Norfolk Southern for strategic of PLASMA: Each locomotive is modeled individually, making it possible to capture both horsepower

Powell, Warren B.

166

Optimizing System Performance Through Dynamic Disk Scheduling Algorithm Selection

Optimizing System Performance Through Dynamic Disk Scheduling Algorithm Selection DANIEL L performance. New approaches and algorithms for disk scheduling have been developed in recent years scheduling of disk requests. Unfortunately, there has yet to be developed a single universal disk

Katchabaw, Michael James

167

Noise-optimal capture for high dynamic range photography

Taking multiple exposures is a well-established approach both for capturing high dynamic range (HDR) scenes and for noise reduction. But what is the optimal set of photos to capture? The typical approach to HDR capture ...

Hasinoff, Samuel William

168

Dynamic optimization of fractionation schedules in radiation therapy

In this thesis, we investigate the improvement in treatment effectiveness when dynamically optimizing the fractionation scheme in radiation therapy. In the first part of the thesis, we consider delivering a different dose ...

Ramakrishnan, Jagdish

2013-01-01

169

An Optimization Framework for Dynamic, Distributed Real-Time Systems

NASA Technical Reports Server (NTRS)

Abstract. This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.

Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara

2003-01-01

170

Dynamic Optimal Control Models in Advertising: Recent Developments

This paper presents a review of recent developments that have taken place in the area of dynamic optimal control models in advertising subsequent to the comprehensive survey of the literature by Sethi in 1977. The basic problem underlying these models is that of determining optimal advertising expenditures and possibly other variables of interest over time for a firm or a

Gustav Feichtinger; Richard F. Hartl; Suresh P. Sethi

1994-01-01

171

An Optimal Dynamic Mechanism for Multi-Armed Bandit Processes

We consider the problem of revenue-optimal dynamic mechanism design in settings where agents' types evolve over time as a function of their (both public and private) experience with items that are auctioned repeatedly over an infinite horizon. A central question here is under- standing what natural restrictions on the environment permit the design of optimal mechanisms (note that even in

Sham M. Kakade; Ilan Lobel; Hamid Nazerzadeh

2010-01-01

172

Review of dynamic optimization methods in renewable natural resource management

In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. The applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.

Williams, B.K.

1989-01-01

173

Optimal Lending Contracts and Firm Dynamics1

We develop a general model of lending in the presence of endogenous borrowing constraints. Borrowing constraints arise because borrowers face limited liability and debt repayment cannot be perfectly enforced. In the model, the dynamics of debt are closely linked with the dynamics of borrowing constraints. In fact, borrowing constraints must satisfy a dynamic consistency requirement: the value of outstanding debt

Rui Albuquerque; Hugo A. Hopenhayn

2002-01-01

174

Optimal Lending Contracts and Firm Dynamics

We develop a general model of lending in the presence of endogenous borrowing constraints. Borrowing constraints arise because borrowers face limited liability and debt repayment cannot be perfectly enforced. In the model, the dynamics of debt are closely linked with the dynamics of borrowing constraints. In fact, borrowing constraints must satisfy a dynamic consistency requirement: the value of outstanding debt

Rui Albuquerque; Hugo A. Hopenhayn

2004-01-01

175

February 15, 2006 Dynamic Optimization for

;RTO - Offline Case Studies performed at least weekly 0.0 20.0 40.0 60.0 80.0 100.0 % Explain online communicated for higher level decisions Planning Scheduling Site-wide Optimization Real-time Optimization Model

Grossmann, Ignacio E.

176

Vehicle dynamics applications of optimal control theory

The aim of the paper is to survey the various forms of optimal-control theory which have been applied to automotive problems and to present illustrative examples of applications studies, with assessments of the state of the art and of the contributions made through the use of optimal-control ideas. After a short introduction to the topic mentioning several questions to which

R. S. Sharp; Huei Peng

2011-01-01

177

Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes.

To survive in fluctuating environmental conditions, microorganisms must be able to quickly react to environmental challenges by upregulating the expression of genes encoding metabolic pathways. Here we show that protein abundance and protein synthesis capacity are key factors that determine the optimal strategy for the activation of a metabolic pathway. If protein abundance relative to protein synthesis capacity increases, the strategies shift from the simultaneous activation of all enzymes to the sequential activation of groups of enzymes and finally to a sequential activation of individual enzymes along the pathway. In the case of pathways with large differences in protein abundance, even more complex pathway activation strategies with a delayed activation of low abundance enzymes and an accelerated activation of high abundance enzymes are optimal. We confirm the existence of these pathway activation strategies as well as their dependence on our proposed constraints for a large number of metabolic pathways in several hundred prokaryotes. PMID:23979724

Bartl, Martin; Kötzing, Martin; Schuster, Stefan; Li, Pu; Kaleta, Christoph

2013-01-01

178

Dynamic Wavelength Routing in WDM Networks via Ant Colony Optimization

Dynamic Wavelength Routing in WDM Networks via Ant Colony Optimization Ryan M. Garlick1 and Richard Methodist University, Dallas, TX 75275 USA Abstract. This study considers the routing and wavelength assignment problem (RWA) in optical wavelength-division multiplexed networks. The focus is dynamic traffic

Barr, Richard

179

Bridging Developmental Systems Theory and Evolutionary Psychology Using Dynamic Optimization

ERIC Educational Resources Information Center

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic…

Frankenhuis, Willem E.; Panchanathan, Karthik; Clark Barrett, H.

2013-01-01

180

Dynamic optimization of a plate reactor start-up

states Limiting actuator dynamics Process uncertainties Staffan Haugwitz et al Control of a plate reactor of productivity, finding operating points for parallel reactions... Staffan Haugwitz et al Control of a plateDynamic optimization of a plate reactor start-up Staffan Haugwitz, Per Hagander and John Bagterp

181

Dynamic positioning configuration and its first-order optimization

NASA Astrophysics Data System (ADS)

Traditional geodetic network optimization deals with static and discrete control points. The modern space geodetic network is, on the other hand, composed of moving control points in space (satellites) and on the Earth (ground stations). The network configuration composed of these facilities is essentially dynamic and continuous. Moreover, besides the position parameter which needs to be estimated, other geophysical information or signals can also be extracted from the continuous observations. The dynamic (continuous) configuration of the space network determines whether a particular frequency of signals can be identified by this system. In this paper, we employ the functional analysis and graph theory to study the dynamic configuration of space geodetic networks, and mainly focus on the optimal estimation of the position and clock-offset parameters. The principle of the D-optimization is introduced in the Hilbert space after the concept of the traditional discrete configuration is generalized from the finite space to the infinite space. It shows that the D-optimization developed in the discrete optimization is still valid in the dynamic configuration optimization, and this is attributed to the natural generalization of least squares from the Euclidean space to the Hilbert space. Then, we introduce the principle of D-optimality invariance under the combination operation and rotation operation, and propose some D-optimal simplex dynamic configurations: (1) (Semi) circular configuration in 2-dimensional space; (2) the D-optimal cone configuration and D-optimal helical configuration which is close to the GPS constellation in 3-dimensional space. The initial design of GPS constellation can be approximately treated as a combination of 24 D-optimal helixes by properly adjusting the ascending node of different satellites to realize a so-called Walker constellation. In the case of estimating the receiver clock-offset parameter, we show that the circular configuration, the symmetrical cone configuration and helical curve configuration are still D-optimal. It shows that the given total observation time determines the optimal frequency (repeatability) of moving known points and vice versa, and one way to improve the repeatability is to increase the rotational speed. Under the Newton's law of motion, the frequency of satellite motion determines the orbital altitude. Furthermore, we study three kinds of complex dynamic configurations, one of which is the combination of D-optimal cone configurations and a so-called Walker constellation composed of D-optimal helical configuration, the other is the nested cone configuration composed of n cones, and the last is the nested helical configuration composed of n orbital planes. It shows that an effective way to achieve high coverage is to employ the configuration composed of a certain number of moving known points instead of the simplex configuration (such as D-optimal helical configuration), and one can use the D-optimal simplex solutions or D-optimal complex configurations in any combination to achieve powerful configurations with flexile coverage and flexile repeatability. Alternately, how to optimally generate and assess the discrete configurations sampled from the continuous one is discussed. The proposed configuration optimization framework has taken the well-known regular polygons (such as equilateral triangle and quadrangular) in two-dimensional space and regular polyhedrons (regular tetrahedron, cube, regular octahedron, regular icosahedron, or regular dodecahedron) into account. It shows that the conclusions made by the proposed technique are more general and no longer limited by different sampling schemes. By the conditional equation of D-optimal nested helical configuration, the relevance issues of GNSS constellation optimization are solved and some examples are performed by GPS constellation to verify the validation of the newly proposed optimization technique. The proposed technique is potentially helpful in maintenance and quadratic optimization of single GNSS of which the orbi

Xue, Shuqiang; Yang, Yuanxi; Dang, Yamin; Chen, Wu

2014-02-01

182

Structural optimization of rotor blades with integrated dynamics and aerodynamics

NASA Technical Reports Server (NTRS)

The problem of structural optimization of helicopter rotor blades with integrated dynamic and aerodynamic design considerations is addressed. Results of recent optimization work on rotor blades for minimum weight with constraints on multiple coupled natural flap-lag frequencies, blade autorotational inertia and centrifugal stress has been reviewed. A strategy has been defined for the ongoing activities in the integrated dynamic/aerodynamic optimization of rotor blades. As a first step, the integrated dynamic/airload optimization problem has been formulated. To calculate system sensitivity derivatives necessary for the optimization recently developed, Global Sensitivity Equations (GSE) are being investigated. A need for multiple objective functions for the integrated optimization problem has been demonstrated and various techniques for solving the multiple objective function optimization are being investigated. The method called the Global Criteria Approach has been applied to a test problem with the blade in vacuum and the blade weight and the centrifugal stress as the multiple objectives. The results indicate that the method is quite effective in solving optimization problems with conflicting objective functions.

Chattopadhyay, Aditi; Walsh, Joanne L.

1988-01-01

183

Structural optimization of rotor blades with integrated dynamics and aerodynamics

NASA Technical Reports Server (NTRS)

The problem of structural optimization of helicopter rotor blades with integrated dynamic and aerodynamic design considerations is addressed. Results of recent optimization work on rotor blades for minimum weight with constraints on multiple coupled natural flap-lag frequencies, blade autorotational inertia and centrifugal stress has been reviewed. A strategy has been defined for the ongoing activities in the integrated dynamic/aerodynamic optimization of rotor blades. As a first step, the integrated dynamic/airload optimization problem has been formulated. To calculate system sensitivity derivatives necessary for the optimization recently developed, Global Sensitivity Equations (GSE) are being investigated. A need for multiple objective functions for the integrated optimization problem has been demonstrated and various techniques for solving the multiple objective function optimization are being investigated. The method called the Global Criteria Approach has been applied to a test problem with the blade in vacuum and the blade weight and the centrifugal stress as the multiple objectives. The results indicate that the method is quite effective in solving optimization problems with conflicting objective functions.

Chattopadhyay, Aditi; Walsh, Joanne L.

1989-01-01

184

Integrated aerodynamic/dynamic optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

An integrated aerodynamic/dynamic optimization procedure is used to minimize blade weight and 4 per rev vertical hub shear for a rotor blade in forward flight. The coupling of aerodynamics and dynamics is accomplished through the inclusion of airloads which vary with the design variables during the optimization process. Both single and multiple objective functions are used in the optimization formulation. The Global Criteria Approach is used to formulate the multiple objective optimization and results are compared with those obtained by using single objective function formulations. Constraints are imposed on natural frequencies, autorotational inertia, and centrifugal stress. The program CAMRAD is used for the blade aerodynamic and dynamic analyses, and the program CONMIN is used for the optimization. Since the spanwise and the azimuthal variations of loading are responsible for most rotor vibration and noise, the vertical airload distributions on the blade, before and after optimization, are compared. The total power required by the rotor to produce the same amount of thrust for a given area is also calculated before and after optimization. Results indicate that integrated optimization can significantly reduce the blade weight, the hub shear and the amplitude of the vertical airload distributions on the blade and the total power required by the rotor.

Chattopadhyay, Aditi; Walsh, Joanne L.; Riley, Michael F.

1989-01-01

185

Incremental Dynamic Programming for On-Line Adaptive Optimal Control

INCREMENTAL DYNAMIC PROGRAMMING FORON--LINE ADAPTIVE OPTIMAL CONTROLSEPTEMBER 1994STEVEN J. BRADTKEB.S., MICHIGAN STATE UNIVERSITYM.S., UNIVERSITY OF MICHIGANPh.D., UNIVERSITY OF MASSACHUSETTS AMHERSTDirected by: Professor Andrew G. BartoReinforcement learning algorithms based on the principles of Dynamic Programming(DP) have enjoyed a great deal of recent attention both empirically and theoretically.These algorithms have been referred to generically as Incremental Dynamic...

B. Erik Ydstie; Paul E. Utgoff; Steven J. Bradtke; W. Richards Adrion

1994-01-01

186

Optimal control of molecular motion expressed through quantum fluid dynamics

NASA Astrophysics Data System (ADS)

A quantum fluid-dynamic (QFD) control formulation is presented for optimally manipulating atomic and molecular systems. In QFD the control quantum system is expressed in terms of the probability density ? and the quantum current j. This choice of variables is motivated by the generally expected slowly varying spatial-temporal dependence of the fluid-dynamical variables. The QFD approach is illustrated for manipulation of the ground electronic state dynamics of HCl induced by an external electric field.

Dey, Bijoy K.; Rabitz, Herschel; Askar, Attila

2000-04-01

187

Dynamic security risk assessment and optimization of power transmission system

The paper presents a practical dynamic security region (PDSR) based dynamic security risk assessment and optimization model\\u000a for power transmission system. The cost of comprehensive security control and the influence of uncertainties of power injections\\u000a are considered in the model of dynamic security risk assessment. The transient stability constraints and uncertainties of\\u000a power injections can be considered easily by PDSR

YiXin Yu; DongTao Wang

2008-01-01

188

Dynamic optimization of chemotherapy outpatient scheduling with uncertainty.

Chemotherapy outpatient scheduling is a complex, dynamic, uncertain problem. Chemotherapy centres are facing increasing demands and they need to increase their efficiency; however there are very few studies looking at using optimization technology on the chemotherapy scheduling problem. We address dynamic uncertainty that arises from requests for appointments that arrive in real time and uncertainty due to last minute scheduling changes. We propose dynamic template scheduling, a novel technique that combines proactive and online optimization and we apply it to the chemotherapy outpatient scheduling problem. We create a proactive template of an expected day in the chemotherapy centre using a deterministic optimization model and a sample of appointments. As requests for appointments arrive, we use the template to schedule them. When a request arrives that does not fit the template, we update the template online using the optimization model and a revised set of appointments. To accommodate last minute additions and cancellations to the schedule, we propose a shuffling algorithm that moves appointment start times within a predefined time limit. We test the use of dynamic template scheduling against the optimal offline solution and the actual performance of the cancer centre. We find improvements in makespan of up to 20 % when using dynamic template scheduling compared to current practice. PMID:24477637

Hahn-Goldberg, Shoshana; Carter, Michael W; Beck, J Christopher; Trudeau, Maureen; Sousa, Philomena; Beattie, Kathy

2014-12-01

189

Structural optimization with dynamic behavior constraints

NASA Technical Reports Server (NTRS)

The minimum weight optimum design of damped linearly elastic structural systems subjected to periodic loading with behavior constraints on maximum deflections and side constraints on design variables is addressed. Attention is focused on the two major impediments to an optimal solution: (1) the time parametric nature of the behavior constraints; and (2) the severe nonconvexity of the design space. A solution method based on upper bound approximations for the behavior constraints and an innovative mathematical programming scheme for seeking the optimal frequency subspace is set forth. Numerical results for several test problems illustrate the effectiveness of the method reported.

Mills-Curran, W. C.; Schmit, L. A.

1983-01-01

190

Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

A fully integrated aerodynamic/dynamic optimization procedure is described for helicopter rotor blades. The procedure combines performance and dynamic analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuvers; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case, the objective function involves power required (in hover, forward flight and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.

1992-01-01

191

Global dynamic optimization approach to predict activation in metabolic pathways

Background During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. Results In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. Conclusions The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints. PMID:24393148

2014-01-01

192

Nonlinear FOPDT model identification for the superheat dynamic in a refrigeration system

An on-line nonlinear FOPDT system identification method is proposed and applied to model the superheat dynamic in a supermarket refrigeration system. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its parameters are time dependent. After the considered system is discretized, the nonlinear FOPDT identification problem is formulated as a Mixed Integer Non-Linear

Zhenyu Yang; Zhen Sun; Casper Andersen

2011-01-01

193

MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II

In this paper we present a multiobjective approach to the dynamic aperture (DA) optimization. Taking the NSLS-II lattice as an example, we have used both sextupoles and quadrupoles as tuning variables to optimize both on-momentum and off-momentum DA. The geometric and chromatic sextupoles are used for nonlinear properties while the tunes are independently varied by quadrupoles. The dispersion and emittance are fixed during tunes variation. The algorithms, procedures, performances and results of our optimization of DA will be discussed and they are found to be robust, general and easy to apply to similar problems.

Yang, L.; Li, Y.; Guo, W.; Krinsky, S.

2011-03-28

194

Policy Optimization for Dynamic Power Management

Dynamic power management schemes (also called policies) reduce the power consumption of complex electronic sys- tems by trading off performance for power in a controlled fash- ion, taking system workload into account. In a power-managed system it is possible to set components into different states, each characterized by performance and power consumption levels. The main function of a power management

Luca Benini; Alessandro Bogliolo; Giuseppe A. Paleologo; GiovanniDe Micheli

1999-01-01

195

Practicing JUDO: Java under dynamic optimizations

ABSTRACT A high - performance implementation of a Java Virtual Machine (JVM) consists of efficient implementation of Just - In - Time (JIT) compilation, exception handling, synchronization mechanism, and garbage collection (GC) These components are tightly coupled to achieve high performance In this paper, we present some static and dynamic techniques implemented in the JIT compilation and exception handling of

Michal Cierniak; Guei-Yuan Lueh; James M. Stichnoth

2000-01-01

196

A cavity approach to optimization and inverse dynamical problems

In these two lectures we shall discuss how the cavity approach can be used efficiently to study optimization problems with global (topological) constraints and how the same techniques can be generalized to study inverse problems in irreversible dynamical processes. These two classes of problems are formally very similar: they both require an efficient procedure to trace over all trajectories of either auxiliary variables which enforce global constraints, or directly dynamical variables defining the inverse dynamical problems. We will mention three basic examples, namely the Minimum Steiner Tree problem, the inverse threshold linear dynamical problem, and the zero patient problem in epidemic cascades. All these examples are root problems in optimization and inference over networks. They appear in many modern applications and in a variety of different contexts. Credit for these results should be shared with A. Braunstein, A. Ramezanpour, F. Altarelli, L. Dall'Asta, and A. Lage-Castellanos.

Lage-Castellanos, Alejandro; Zecchina, Riccardo

2014-01-01

197

On Using Cutting Planes in Pseudo-Boolean Optimization

Cutting planes are a well-known, widely used, and very eectiv e technique for Integer Linear Programming (ILP). However, cutting plane techniques are seldom used in Pseudo- Boolean Optimization (PBO) algorithms. This paper addresses the utilization of Gomory mixed-integer and clique cuts, in Satisabilit y-based algorithms for PBO, and shows how these cuts can be used for computing lower bounds and

Vasco M. Manquinho; João P. Marques Silva

2006-01-01

198

Dynamic optimization of district energy grid

NASA Astrophysics Data System (ADS)

The University of Iowa Power Plant operates utility generation and distribution for campus facilities, including electricity, steam, and chilled water. It is desirable to evaluate the optimal load combination of boilers, engines and chillers to meet the demand at minimal cost, particularly for future demand scenarios. An algorithm has been developed which takes into account the performance of individual units as part of the mix which ultimately supplies the campus and determine the degree that each should be operating to most efficiently meet demand. The algorithm is part of an integrated simulation tool which is specifically designed to apply traditional optimization techniques for a given (both current and possible) circumstance. The second component is to couple the algorithm with accurate estimates and historical data through which expected demand could be predicted. The simulation tool can account for any theoretical circumstance, which will be highly beneficial for strategic planning. As part of the process it is also necessary to determine the unique operating characteristics of the system components. The algorithms rely upon performance curves of individual system components (boiler, chiller, etc.) and those must be developed and refined when possible from experimental testing and commissioning or manufacturer supplied data.

Salsbery, Scott

199

Dynamic backlight scaling optimization for mobile streaming applications

With the increasing variety of mobile applications, reducing the energy consumption of mobile devices is a major challenge in sus- taining multimedia streaming applications. This paper explores backlight scaling, which is deemed a promising technical solution. First, we model the problem as a dynamic backlight scaling optimization problem. The objective is to minimize the energy consumption of the backlight when

Pi-Cheng Hsiu; Chun-Han Lin; Cheng-Kang Hsieh

2011-01-01

200

Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms

of the characteristics needed to adopt complex goal-oriented behaviors. Bubbles can indeed shape the attractor landscape: they either lead to the saturation of the field, the lack of any coherent activity, or the selfExploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms Jean

Boyer, Edmond

201

Optimal dispatch in dynamic security constrained open power market

Power system security is a new concern in the competitive power market operation, because the integration of the system controller and the generation owner has been broken. This paper presents an approach for dynamic security constrained optimal dispatch in restructured power market environment. The transient energy margin using transient energy function (TEF) approach has been used to calculate the stability

S. N. Singh; A. K. David

2002-01-01

202

Optimal dynamic policies for product and process innovation

Relatively few studies have analytically examined incremental product and process R & D activities after a product is in the marketplace. In this paper, new product demand is modeled as a dynamic function of price and incremental product innovation, while process improvements influence costs. Optimal price, and product and process R & D expenditure patterns are studied analytically and with

Barry L. Bayus

1995-01-01

203

OPTIMAL DESIGN AND DYNAMIC SIMULATION OF A HYBRID SOLAR VEHICLE

The paper deals with a detailed study on the optimal sizing of a solar hybrid car, based on a longitudinal vehicle dynamic model and considering energy flows, weight and costs. The model describes the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs. It is shown that significant

Ivan Arsie; Gianfranco Rizzo; Marco Sorrentino

204

Power System State Estimation with Dynamic Optimal Measurement Selection

Power System State Estimation with Dynamic Optimal Measurement Selection Jinghe Zhang, Greg Welch-time estimation a major challenge. In this paper we present the Lower Dimensional Measurement- space (LoDiM) state and complexity of intercon- nected power networks, state estimation computation remains one of the primary

Welch, Greg

205

OPTIMAL CONTROL OF ATOMIC, MOLECULAR AND ELECTRON DYNAMICS

to be able to observe in a passive way, but in fact also to actively control quantum mechanical processesChapter 9 OPTIMAL CONTROL OF ATOMIC, MOLECULAR AND ELECTRON DYNAMICS WITH TAILORED FEMTOSECOND Am Hubland, 97074 WÃ¼rzburg, Germany Matthias Wollenhaupt and Thomas Baumert Institute of Physics

Kassel, UniversitÃ¤t

206

Dynamic vs. Static Optimization Techniques for Object-Oriented Languages

-oriented languages are type feedback (dynamic) and concrete type inference (static). We directly compare the two optimizations, can lead to poor run-time perfor- mance. Thus, the key to efficient implementation of object previous executions of the program to determine the set possible receiver classes, and Â· Concrete type

HÃ¶lzle, Urs

207

Deriving Optimized Integrity Monitoring Triggers from Dynamic Integrity Constraints

Modern approaches to integrity monitoring in active databases suggest to generate triggers from constraints as part of database design and to utilize constraint simplication techniques for trigger optimization. Such proposals, however, have been restricted to static conditions only. In this paper, we show how to derive triggers from dynamic integrity constraints which describe properties of state sequences and which can

Michael Gertz; Udo W. Lipeck

1996-01-01

208

HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols

HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols B. M. Adams 1 , H. T. Banks in model- ing HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical

209

Multiobjective Optimization of Low-Energy Trajectories Using Optimal Control on Dynamical Channels

NASA Technical Reports Server (NTRS)

We introduce a computational method to design efficient low-energy trajectories by extracting initial solutions from dynamical channels formed by invariant manifolds, and improving these solutions through variational optimal control. We consider trajectories connecting two unstable periodic orbits in the circular restricted 3-body problem (CR3BP). Our method leverages dynamical channels to generate a range of solutions, and approximates the areto front for impulse and time of flight through a multiobjective optimization of these solutions based on primer vector theory. We demonstrate the application of our method to a libration orbit transfer in the Earth-Moon system.

Coffee, Thomas M.; Anderson, Rodney L.; Lo, Martin W.

2011-01-01

210

A dynamic optimization model for solid waste recycling.

Recycling is an important part of waste management (that includes different kinds of issues: environmental, technological, economic, legislative, social, etc.). Differently from many works in literature, this paper is focused on recycling management and on the dynamic optimization of materials collection. The developed dynamic decision model is characterized by state variables, corresponding to the quantity of waste in each bin per each day, and control variables determining the quantity of material that is collected in the area each day and the routes for collecting vehicles. The objective function minimizes the sum of costs minus benefits. The developed decision model is integrated in a GIS-based Decision Support System (DSS). A case study related to the Cogoleto municipality is presented to show the effectiveness of the proposed model. From optimal results, it has been found that the net benefits of the optimized collection are about 2.5 times greater than the estimated current policy. PMID:23158873

Anghinolfi, Davide; Paolucci, Massimo; Robba, Michela; Taramasso, Angela Celeste

2013-02-01

211

Analysis and Optimization of Pulse Dynamics for Magnetic Stimulation

Magnetic stimulation is a standard tool in brain research and has found important clinical applications in neurology, psychiatry, and rehabilitation. Whereas coil designs and the spatial field properties have been intensively studied in the literature, the temporal dynamics of the field has received less attention. Typically, the magnetic field waveform is determined by available device circuit topologies rather than by consideration of what is optimal for neural stimulation. This paper analyzes and optimizes the waveform dynamics using a nonlinear model of a mammalian axon. The optimization objective was to minimize the pulse energy loss. The energy loss drives power consumption and heating, which are the dominating limitations of magnetic stimulation. The optimization approach is based on a hybrid global-local method. Different coordinate systems for describing the continuous waveforms in a limited parameter space are defined for numerical stability. The optimization results suggest that there are waveforms with substantially higher efficiency than that of traditional pulse shapes. One class of optimal pulses is analyzed further. Although the coil voltage profile of these waveforms is almost rectangular, the corresponding current shape presents distinctive characteristics, such as a slow low-amplitude first phase which precedes the main pulse and reduces the losses. Representatives of this class of waveforms corresponding to different maximum voltages are linked by a nonlinear transformation. The main phase, however, scales with time only. As with conventional magnetic stimulation pulses, briefer pulses result in lower energy loss but require higher coil voltage than longer pulses. PMID:23469168

Goetz, Stefan M.; Truong, Cong Nam; Gerhofer, Manuel G.; Peterchev, Angel V.; Herzog, Hans-Georg; Weyh, Thomas

2013-01-01

212

Optimal Control of HIV Dynamic Using Embedding Method

This present study proposes an optimal control problem, with the final goal of implementing an optimal treatment protocol which could maximize the survival time of patients and minimize the cost of drug utilizing a system of ordinary differential equations which describes the interaction of the immune system with the human immunodeficiency virus (HIV). Optimal control problem transfers into a modified problem in measure space using an embedding method in which the existence of optimal solution is guaranteed by compactness of the space. Then the metamorphosed problem is approximated by a linear programming (LP) problem, and by solving this LP problem a suboptimal piecewise constant control function, which is more practical from the clinical viewpoint, is achieved. The comparison between the immune system dynamics in treated and untreated patients is introduced. Finally, the relationships between the healthy cells and virus are shown. PMID:21687584

Zarei, H.; Kamyad, A. V.; Farahi, M. H.

2011-01-01

213

Experimental Testing of Dynamically Optimized Photoelectron Beams

NASA Astrophysics Data System (ADS)

We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC photoinjector. The scheme under study employs the tendency of intense electron beams to rearrange to produce uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation. We show that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.

Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Serafini, L.; Vicario, C.; Jones, S.

2006-11-01

214

Optimal dynamic bandwidth allocation for complex networks

NASA Astrophysics Data System (ADS)

Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.

Jiang, Zhong-Yuan; Liang, Man-Gui; Li, Qian; Guo, Dong-Chao

2013-03-01

215

Optimization of cardiovascular stent design using computational fluid dynamics.

Coronary stent design affects the spatial distribution of wall shear stress (WSS), which can influence the progression of endothelialization, neointimal hyperplasia, and restenosis. Previous computational fluid dynamics (CFD) studies have only examined a small number of possible geometries to identify stent designs that reduce alterations in near-wall hemodynamics. Based on a previously described framework for optimizing cardiovascular geometries, we developed a methodology that couples CFD and three-dimensional shape-optimization for use in stent design. The optimization procedure was fully-automated, such that solid model construction, anisotropic mesh generation, CFD simulation, and WSS quantification did not require user intervention. We applied the method to determine the optimal number of circumferentially repeating stent cells (N(C)) for slotted-tube stents with various diameters and intrastrut areas. Optimal stent designs were defined as those minimizing the area of low intrastrut time-averaged WSS. Interestingly, we determined that the optimal value of N(C) was dependent on the intrastrut angle with respect to the primary flow direction. Further investigation indicated that stent designs with an intrastrut angle of approximately 40 deg minimized the area of low time-averaged WSS regardless of vessel size or intrastrut area. Future application of this optimization method to commercially available stent designs may lead to stents with superior hemodynamic performance and the potential for improved clinical outcomes. PMID:22482657

Gundert, Timothy J; Marsden, Alison L; Yang, Weiguang; LaDisa, John F

2012-01-01

216

Experimental Testing of Dynamically Optimized Photoelectron Beams

NASA Astrophysics Data System (ADS)

We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC (located at INFN-LNF, Frascati) photoinjector. The scheme under study employs the natural tendency in intense electron beams to configure themselves to produce a uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation, We show that the existing infrastructure at SPARC is nearly ideal for the proposed tests, and that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.

Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Pirro, G. Di; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Serafini, L.; Vicario, C.

2007-09-01

217

Experimental Testing of Dynamically Optimized Photoelectron Beams

NASA Astrophysics Data System (ADS)

We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC (located at INFN-LNF, Frascati) photoinjector. The scheme under study employs the natural tendency in intense electron beams to configure themselves to produce a uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation, We show that the existing infrastructure at SPARC is nearly ideal for the proposed tests, and that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.

Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Serafini, L.; Vicario, C.

218

An approach combining stochastic simulations and dynamic optimization is constructed to decide the optimal funding policy of the defined benefit pension scheme. The results show a significant advantage and flexibility of this approach in projecting the optimal financial status over the traditional deterministic pension valuation. In this study, the optimal contributions are estimated through dynamic programming under the projected workforce

Shih-Chieh Chang

1999-01-01

219

The Performance Potential of Trace-based Dynamic Optimization Brian Fahs Aqeel Mahesri Francesco of Illinois at Urbana-Champaign Abstract Dynamic optimization can apply powerful optimizations to hot execution paths that span traditional boundaries such as branches and calls, including calls to dynamic

Lumetta, Steve

220

Optimally combining dynamical decoupling and quantum error correction.

Quantum control and fault-tolerant quantum computing (FTQC) are two of the cornerstones on which the hope of realizing a large-scale quantum computer is pinned, yet only preliminary steps have been taken towards formalizing the interplay between them. Here we explore this interplay using the powerful strategy of dynamical decoupling (DD), and show how it can be seamlessly and optimally integrated with FTQC. To this end we show how to find the optimal decoupling generator set (DGS) for various subspaces relevant to FTQC, and how to simultaneously decouple them. We focus on stabilizer codes, which represent the largest contribution to the size of the DGS, showing that the intuitive choice comprising the stabilizers and logical operators of the code is in fact optimal, i.e., minimizes a natural cost function associated with the length of DD sequences. Our work brings hybrid DD-FTQC schemes, and their potentially considerable advantages, closer to realization. PMID:23559088

Paz-Silva, Gerardo A; Lidar, D A

2013-01-01

221

Optimal Brain Surgeon for General Dynamic Neural Networks

This paper presents a pruning algorithm based on optimal brain surgeon (OBS) for general dynamic neural networks (GDNN). The\\u000a pruning algorithm uses Hessian information and considers the order of time delay for saliency calculation. In GDNNs all layers\\u000a have feedback connections with time delays to the same and to all other layers. The parameters are trained with the Levenberg-Marquardt\\u000a (LM)

Christian Endisch; Christoph Hackl; Dierk Schröder

2007-01-01

222

Dynamic-CoMPI: dynamic optimization techniques for MPI parallel applications

This work presents an optimization of MPI communications, called Dynamic-CoMPI, which uses two techniques in order to reduce the impact of communications and non-contiguous I\\/O requests in parallel applications.\\u000a These techniques are independent of the application and complementaries to each other. The first technique is an optimization\\u000a of the Two-Phase collective I\\/O technique from ROMIO, called Locality aware strategy for

Rosa Filgueira; Jesús Carretero; David E. Singh; Alejandro Calderón; Alberto Núñez

223

Optimization of Dynamic Aperture of PEP-X Baseline Design

SLAC is developing a long-range plan to transfer the evolving scientific programs at SSRL from the SPEAR3 light source to a much higher performing photon source. Storage ring design is one of the possibilities that would be housed in the 2.2-km PEP-II tunnel. The design goal of PEPX storage ring is to approach an optimal light source design with horizontal emittance less than 100 pm and vertical emittance of 8 pm to reach the diffraction limit of 1-{angstrom} x-ray. The low emittance design requires a lattice with strong focusing leading to high natural chromaticity and therefore to strong sextupoles. The latter caused reduction of dynamic aperture. The dynamic aperture requirement for horizontal injection at injection point is about 10 mm. In order to achieve the desired dynamic aperture the transverse non-linearity of PEP-X is studied. The program LEGO is used to simulate the particle motion. The technique of frequency map is used to analyze the nonlinear behavior. The effect of the non-linearity is tried to minimize at the given constrains of limited space. The details and results of dynamic aperture optimization are discussed in this paper.

Wang, Min-Huey; /SLAC; Cai, Yunhai; /SLAC; Nosochkov, Yuri; /SLAC; ,

2010-08-23

224

Human opinion dynamics: An inspiration to solve complex optimization problems

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making. PMID:24141795

Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan

2013-01-01

225

Human opinion dynamics: an inspiration to solve complex optimization problems.

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making. PMID:24141795

Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P; Kapur, Pawan

2013-01-01

226

Optimal forwarding ratio on dynamical networks with heterogeneous mobility

NASA Astrophysics Data System (ADS)

Since the discovery of non-Poisson statistics of human mobility trajectories, more attention has been paid to understand the role of these patterns in different dynamics. In this study, we first introduce the heterogeneous mobility of mobile agents into dynamical networks, and then investigate packet forwarding strategy on the heterogeneous dynamical networks. We find that the faster speed and the higher proportion of high-speed agents can enhance the network throughput and reduce the mean traveling time in random forwarding. A hierarchical structure in the dependence of high-speed is observed: the network throughput remains unchanged at small and large high-speed value. It is also interesting to find that a slightly preferential forwarding to high-speed agents can maximize the network capacity. Through theoretical analysis and numerical simulations, we show that the optimal forwarding ratio stems from the local structural heterogeneity of low-speed agents.

Gan, Yu; Tang, Ming; Yang, Hanxin

2013-05-01

227

Human opinion dynamics: An inspiration to solve complex optimization problems

NASA Astrophysics Data System (ADS)

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.

Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan

2013-10-01

228

Exposure Time Optimization for Highly Dynamic Star Trackers

Under highly dynamic conditions, the star-spots on the image sensor of a star tracker move across many pixels during the exposure time, which will reduce star detection sensitivity and increase star location errors. However, this kind of effect can be compensated well by setting an appropriate exposure time. This paper focuses on how exposure time affects the star tracker under highly dynamic conditions and how to determine the most appropriate exposure time for this case. Firstly, the effect of exposure time on star detection sensitivity is analyzed by establishing the dynamic star-spot imaging model. Then the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm. Combining these analyses, the effect of exposure time on attitude accuracy is finally determined. Some simulations are carried out to validate these effects, and the results show that there are different optimal exposure times for different angular velocities of a star tracker with a given configuration. In addition, the results of night sky experiments using a real star tracker agree with the simulation results. The summarized regularities in this paper should prove helpful in the system design and dynamic performance evaluation of the highly dynamic star trackers. PMID:24618776

Wei, Xinguo; Tan, Wei; Li, Jian; Zhang, Guangjun

2014-01-01

229

Dynamic optimization of bioprocesses: efficient and robust numerical strategies.

The dynamic optimization (open loop optimal control) of non-linear bioprocesses is considered in this contribution. These processes can be described by sets of non-linear differential and algebraic equations (DAEs), usually subject to constraints in the state and control variables. A review of the available solution techniques for this class of problems is presented, highlighting the numerical difficulties arising from the non-linear, constrained and often discontinuous nature of these systems. In order to surmount these difficulties, we present several alternative stochastic and hybrid techniques based on the control vector parameterization (CVP) approach. The CVP approach is a direct method which transforms the original problem into a non-linear programming (NLP) problem, which must be solved by a suitable (efficient and robust) solver. In particular, a hybrid technique uses a first global optimization phase followed by a fast second phase based on a local deterministic method, so it can handle the nonconvexity of many of these NLPs. The efficiency and robustness of these techniques is illustrated by solving several challenging case studies regarding the optimal control of fed-batch bioreactors and other bioprocesses. In order to fairly evaluate their advantages, a careful and critical comparison with several other direct approaches is provided. The results indicate that the two-phase hybrid approach presents the best compromise between robustness and efficiency. PMID:15888349

Banga, Julio R; Balsa-Canto, Eva; Moles, Carmen G; Alonso, Antonio A

2005-06-29

230

A mathematical programming approach to stochastic and dynamic optimization problems

We propose three ideas for constructing optimal or near-optimal policies: (1) for systems for which we have an exact characterization of the performance space we outline an adaptive greedy algorithm that gives rise to indexing policies (we illustrate this technique in the context of indexable systems); (2) we use integer programming to construct policies from the underlying descriptions of the performance space (we illustrate this technique in the context of polling systems); (3) we use linear control over polyhedral regions to solve deterministic versions for this class of problems. This approach gives interesting insights for the structure of the optimal policy (we illustrate this idea in the context of multiclass queueing networks). The unifying theme in the paper is the thesis that better formulations lead to deeper understanding and better solution methods. Overall the proposed approach for stochastic and dynamic optimization parallels efforts of the mathematical programming community in the last fifteen years to develop sharper formulations (polyhedral combinatorics and more recently nonlinear relaxations) and leads to new insights ranging from a complete characterization and new algorithms for indexable systems to tight lower bounds and new algorithms with provable a posteriori guarantees for their suboptimality for polling systems, multiclass queueing and loss networks.

Bertsimas, D.

1994-12-31

231

Instrumenting V8 to Measure the Efficacy of Dynamic Optimizations on Production Code

Instrumenting V8 to Measure the Efficacy of Dynamic Optimizations on Production Code Michael MaassScript, virtual machine, dynamic optimization, measurement, instru- mentation #12;Abstract As JavaScript has risen of compilers designed to optimize JavaScript speed. Public one-upmanship has played out between these browsers

232

In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM

C. Dai; Y. P. Li; G. H. Huang

2011-01-01

233

Dynamic stochastic optimization models for air traffic flow management

NASA Astrophysics Data System (ADS)

This dissertation presents dynamic stochastic optimization models for Air Traffic Flow Management (ATFM) that enables decisions to adapt to new information on evolving capacities of National Airspace System (NAS) resources. Uncertainty is represented by a set of capacity scenarios, each depicting a particular time-varying capacity profile of NAS resources. We use the concept of a scenario tree in which multiple scenarios are possible initially. Scenarios are eliminated as possibilities in a succession of branching points, until the specific scenario that will be realized on a particular day is known. Thus the scenario tree branching provides updated information on evolving scenarios, and allows ATFM decisions to be re-addressed and revised. First, we propose a dynamic stochastic model for a single airport ground holding problem (SAGHP) that can be used for planning Ground Delay Programs (GDPs) when there is uncertainty about future airport arrival capacities. Ground delays of non-departed flights can be revised based on updated information from scenario tree branching. The problem is formulated so that a wide range of objective functions, including non-linear delay cost functions and functions that reflect equity concerns can be optimized. Furthermore, the model improves on existing practice by ensuring efficient use of available capacity without necessarily exempting long-haul flights. Following this, we present a methodology and optimization models that can be used for decentralized decision making by individual airlines in the GDP planning process, using the solutions from the stochastic dynamic SAGHP. Airlines are allowed to perform cancellations, and re-allocate slots to remaining flights by substitutions. We also present an optimization model that can be used by the FAA, after the airlines perform cancellation and substitutions, to re-utilize vacant arrival slots that are created due to cancellations. Finally, we present three stochastic integer programming models for managing inbound air traffic flow of an airport, when there is adverse weather impacting the arrival capacity of the airport along with its arrival fixes. These are the first models, for optimizing ATFM decisions, which address uncertainty of future capacities of multiple NAS resources.

Mukherjee, Avijit

234

Integrated aerodynamic/dynamic optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

An integrated aerodynamic/dynamic optimization procedure is used to minimize blade weight and 4/rev vertical shear of a rotor blade in forward flight. Both single and multiple objective functions are used, with constraints imposed on the first four coupled natural frequencies (elastic modes only), the blade aerorotational inertia, and the centrifugal stress. The global criteria approach is used for the multiple objective formulation, and the results are compared with those obtained from single objective function formulations. Optimum designs are compared against a reference blade, and it is shown that optimum results can be obtained in 7-10 cycles.

Chattopadhyay, Aditi; Walsh, Joanne L.; Riley, Michael F.

1989-01-01

235

NASA Astrophysics Data System (ADS)

The development and optimization of liquid rocket engines is an integral part of space vehicle design, since most Earth-to-orbit launch vehicles to date have used liquid rockets as their main propulsion system. Rocket engine design tools range in fidelity from very simple conceptual level tools to full computational fluid dynamics (CFD) simulations. The level of fidelity of interest in this research is a design tool that determines engine thrust and specific impulse as well as models the powerhead of the engine. This is the highest level of fidelity applicable to a conceptual level design environment where faster running analyses are desired. The optimization of liquid rocket engines using a powerhead analysis tool is a difficult problem, because it involves both continuous and discrete inputs as well as a nonlinear design space. Example continuous inputs are the main combustion chamber pressure, nozzle area ratio, engine mixture ratio, and desired thrust. Example discrete variable inputs are the engine cycle (staged-combustion, gas generator, etc.), fuel/oxidizer combination, and engine material choices. Nonlinear optimization problems involving both continuous and discrete inputs are referred to as Mixed-Integer Nonlinear Programming (MINLP) problems. Many methods exist in literature for solving MINLP problems; however none are applicable for this research. All of the existing MINLP methods require the relaxation of the discrete variables as part of their analysis procedure. This means that the discrete choices must be evaluated at non-discrete values. This is not possible with an engine powerhead design code. Therefore, a new optimization method was developed that uses modified response surface equations to provide lower bounds of the continuous design space for each unique discrete variable combination. These lower bounds are then used to efficiently solve the optimization problem. The new optimization procedure was used to find optimal rocket engine designs subject to various weight, cost and performance constraints. The results show that the new method efficiently solved the mixed-input optimization problem without requiring discrete variable relaxation.

St. Germain, Brad David

236

Dynamic Simulation and Optimization of Nuclear Hydrogen Production Systems

This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.

Paul I. Barton; Mujid S. Kaximi; Georgios Bollas; Patricio Ramirez Munoz

2009-07-31

237

Dynamic learning rate optimization of the backpropagation algorithm.

It has been observed by many authors that the backpropagation (BP) error surfaces usually consist of a large amount of flat regions as well as extremely steep regions. As such, the BP algorithm with a fixed learning rate will have low efficiency. This paper considers dynamic learning rate optimization of the BP algorithm using derivative information. An efficient method of deriving the first and second derivatives of the objective function with respect to the learning rate is explored, which does not involve explicit calculation of second-order derivatives in weight space, but rather uses the information gathered from the forward and backward propagation, Several learning rate optimization approaches are subsequently established based on linear expansion of the actual outputs and line searches with acceptable descent value and Newton-like methods, respectively. Simultaneous determination of the optimal learning rate and momentum is also introduced by showing the equivalence between the momentum version BP and the conjugate gradient method. Since these approaches are constructed by simple manipulations of the obtained derivatives, the computational and storage burden scale with the network size exactly like the standard BP algorithm, and the convergence of the BP algorithm is accelerated with in a remarkable reduction (typically by factor 10 to 50, depending upon network architectures and applications) in the running time for the overall learning process. Numerous computer simulation results are provided to support the present approaches. PMID:18263352

Yu, X H; Chen, G A; Cheng, S X

1995-01-01

238

Optimal control and cold war dynamics between plant and herbivore.

Herbivores eat the leaves that a plant needs for photosynthesis. However, the degree of antagonism between plant and herbivore may depend critically on the timing of their interactions and the intrinsic value of a leaf. We present a model that investigates whether and when the timing of plant defense and herbivore feeding activity can be optimized by evolution so that their interactions can move from antagonistic to neutral. We assume that temporal changes in environmental conditions will affect intrinsic leaf value, measured as potential carbon gain. Using optimal-control theory, we model herbivore evolution, first in response to fixed plant strategies and then under coevolutionary dynamics in which the plant also evolves in response to the herbivore. In the latter case, we solve for the evolutionarily stable strategies of plant defense induction and herbivore hatching rate under different ecological conditions. Our results suggest that the optimal strategies for both plant and herbivore are to avoid direct conflict. As long as the plant has the capability for moderately lethal defense, the herbivore will modify its hatching rate to avoid plant defenses, and the plant will never have to use them. Insights from this model offer a possible solution to the paradox of sublethal defenses and provide a mechanism for stable plant-herbivore interactions without the need for natural enemy control. PMID:23852361

Low, Candace; Ellner, Stephen P; Holden, Matthew H

2013-08-01

239

Optimal control of vaccination dynamics during an influenza epidemic.

For emerging diseases like pandemic influenza, several factors could impact the outcome of vaccination programs, including a delay in vaccine availability, imperfect vaccine-induced protection, and inadequate number of vaccines to sufficiently lower the susceptibility of the population by raising the level of herd immunity. We sought to investigate the effect of these factors in determining optimal vaccination strategies during an emerging influenza infection for which the population is entirely susceptible. We developed a population dynamical model of disease transmission and vaccination, and analyzed the control problem associated with an adaptive time-dependent vaccination strategy, in which the rate of vaccine distribution is optimally determined with time for minimizing the total number of infections (i.e., the epidemic final size). We simulated the model and compared the outcomes with a constant vaccination strategy in which the rate of vaccine distribution is time-independent. When vaccines are available at the onset of epidemic, our findings show that for a sufficiently high vaccine efficacy, the adaptive and constant vaccination strategies lead to comparable outcomes in terms of the epidemic final size. However, the adaptive vaccination requires a vaccine coverage higher than (or equivalent to) the constant vaccination regardless of the rate of vaccine distribution, suggesting that the latter is a more cost-effective strategy. When the vaccine efficacy is below a certain threshold, the adaptive vaccination could substantially outperform the constant vaccination, and the impact of adaptive strategy becomes more pronounced as the rate of vaccine distribution increases. We observed similar results when vaccines become available with a delay during the epidemic; however, the adaptive strategy may require a significantly higher vaccine coverage to outperform the constant vaccination strategy. The findings indicate that the vaccine efficacy is a key parameter that affects optimal control of vaccination dynamics during an epidemic, raising an important question on the trade-off between effectiveness and cost-effectiveness of vaccination policies in the context of limited vaccine quantities. PMID:25347806

Jaberi-Douraki, Majid; Moghadas, Seyed M

2014-10-01

240

Optimal spatiotemporal reduced order modeling for nonlinear dynamical systems

NASA Astrophysics Data System (ADS)

Proposed in this dissertation is a novel reduced order modeling (ROM) framework called optimal spatiotemporal reduced order modeling (OPSTROM) for nonlinear dynamical systems. The OPSTROM approach is a data-driven methodology for the synthesis of multiscale reduced order models (ROMs) which can be used to enhance the efficiency and reliability of under-resolved simulations for nonlinear dynamical systems. In the context of nonlinear continuum dynamics, the OPSTROM approach relies on the concept of embedding subgrid-scale models into the governing equations in order to account for the effects due to unresolved spatial and temporal scales. Traditional ROMs neglect these effects, whereas most other multiscale ROMs account for these effects in ways that are inconsistent with the underlying spatiotemporal statistical structure of the nonlinear dynamical system. The OPSTROM framework presented in this dissertation begins with a general system of partial differential equations, which are modified for an under-resolved simulation in space and time with an arbitrary discretization scheme. Basic filtering concepts are used to demonstrate the manner in which residual terms, representing subgrid-scale dynamics, arise with a coarse computational grid. Models for these residual terms are then developed by accounting for the underlying spatiotemporal statistical structure in a consistent manner. These subgrid-scale models are designed to provide closure by accounting for the dynamic interactions between spatiotemporal macroscales and microscales which are otherwise neglected in a ROM. For a given resolution, the predictions obtained with the modified system of equations are optimal (in a mean-square sense) as the subgrid-scale models are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation. Methods are suggested for efficient model construction, appraisal, error measure, and implementation with a couple of well-known time-discretization schemes. Four nonlinear dynamical systems serve as testbeds to demonstrate the technique. First we consider an autonomous van der Pol oscillator for which all trajectories evolve to self-sustained limit cycle oscillations. Next we investigate a forced Duffing oscillator for which the response may be regular or chaotic. In order to demonstrate application for a problem in nonlinear wave propagation, we consider the viscous Burgers equation with large-amplitude inflow disturbances. For the fourth and final system, we analyze the nonlinear structural dynamics of a geometrically nonlinear beam under the influence of time-dependent external forcing. The practical utility of the proposed subgrid-scale models is enhanced if it can be shown that certain statistical moments amongst the subgrid-scale dynamics display to some extent the following properties: spatiotemporal homogeneity, ergodicity, smooth scaling with respect to the system parameters, and universality. To this end, we characterize the subgrid-scale dynamics for each of the four problems. The results in this dissertation indicate that temporal homogeneity and ergodicity are excellent assumptions for both regular and chaotic response types. Spatial homogeneity is found to be a very good assumption for the nonlinear beam problem with models based upon single-point but not multi-point spatial stencils. The viscous Burgers flow, however, requires spatially heterogeneous models regardless of the stencil. For each of the four problems, the required statistical moments display a functional dependence which can easily be characterized with respect to the physical parameters and the computational grid. This observed property, in particular, greatly simplifies model construction by way of moment estimation. We investigate the performance of the subgrid-scale models with under-resolved simulations (in space and time) and various discretization schemes. For the canonical Duffing and van der Pol oscillators, the subgrid-scale models are found to improve the accuracy of under-resolved time-marching and time-s

LaBryer, Allen

241

Dynamic Distributed Rate Control for Wireless Networks by Optimal Cartel Maintenance Strategy

Dynamic Distributed Rate Control for Wireless Networks by Optimal Cartel Maintenance Strategy Zhu performance, we develop a distributed rate control algorithm using opti- mal Cartel maintenance strategy

Liu, K. J. Ray

242

A Formal Approach to Empirical Dynamic Model Optimization and Validation

NASA Technical Reports Server (NTRS)

A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.

Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

243

Geometry optimization for micro-pressure sensor considering dynamic interference.

Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz(1/4). The favorable overall performances enable the sensor more suitable for altimetry. PMID:25273764

Yu, Zhongliang; Zhao, Yulong; Li, Lili; Tian, Bian; Li, Cun

2014-09-01

244

Geometry optimization for micro-pressure sensor considering dynamic interference

NASA Astrophysics Data System (ADS)

Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz1/4. The favorable overall performances enable the sensor more suitable for altimetry.

Yu, Zhongliang; Zhao, Yulong; Li, Lili; Tian, Bian; Li, Cun

2014-09-01

245

Molecular-dynamics simulator for optimal control of molecular motion

NASA Astrophysics Data System (ADS)

In recognition of recent interest in developing optimal control techniques for manipulating molecular motion, this paper introduces a computer-driven electromechanical analog of this process. The resultant molecular-dynamics simulator (MDS) is centered around a linear air track for which the atoms of the controlled molecule are simulated as nearly frictionless carts on the track. Bonds in the simulated molecule are described by precision springs, and the interaction with an external optical field is simulated through a computer-based linear driver. When the MDS is operated in the harmonic regime, it can be used as an exact analog of molecular-scale quantum systems through Ehrenfest's theorem or, equivalently, as a classical set of coupled oscillators. The tools of optimal control theory currently being applied at the molecular scale are used to design the forcing function for the MDS. Optical encoders are used to measure bond distances for graphic representation of the MDS behavior. Bond breaking can also be simulated by bond-length-sensitive trigger-release mechanisms. The MDS is especially useful as a modeling tool to bridge theoretical studies and eventual laboratory experiments at the true molecular scale.

Husman, M.; Schwieters, C.; Littman, M.; Rabitz, H.

1991-11-01

246

Search of Initial Conditions for Dynamic Systems using Intelligent Optimization Methods

In this contribution we propose the use of intelligent optimization methods in the search of initial conditions for the analysis of dynamic systems. The use of intelligent optimization methods provides a search tool that does not depend on the experience of the researcher in the particular system to analyze. An example of a dynamic system that models an electrical power

Julio Barrera; Juan J. Flores

2007-01-01

247

Structural and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme

Structural and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme) is an essential enzyme involved in the lysine biosynthesis pathway. DHDPS from E. coli is a homotetramer and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme Dihydrodipicolinate Synthase

Hickman, Mark

248

Flow analysis and nozzle-shape optimization for the cold-gas dynamic-spray process

. For the optimum design of the nozzle, helium as the carrier gas is found to give rise to a substantially higher observations. Keywords: cold-gas dynamic spray process, nozzle design and optimization NOTATION A nozzle crossFlow analysis and nozzle-shape optimization for the cold-gas dynamic-spray process M Grujicic1*, W

Grujicic, Mica

249

Dynamic online optimization of a house heating system in a fluctuating energy price

Dynamic online optimization of a house heating system in a fluctuating energy price scenario in this problem is the time- varying nature of the main disturbances, which are the energy price and outdoor that there is a great economical gain in using dynamic optimization for the case of variable energy price. 1

Skogestad, Sigurd

250

External optimal control of self-organisation dynamics in a chemotaxis reaction diffusion system

describe numerical simulations and optimal control of E. coli bacterial chemotaxis. We choose a parabolic of self-organising biological systems, we describe numerical optimal control of E. coli bacterialExternal optimal control of self-organisation dynamics in a chemotaxis reaction diffusion system D

Maurer, Helmut

251

Optimal path for China's strategic petroleum reserve: A dynamic programming analysis

This paper proposes a dynamic programming model to explore the optimal stockpiling path for China's strategic petroleum reserve before 2020. The optimal oil acquisition sizes in 2008–2020 under different scenarios are estimated. The effects of oil price, risks and elasticity value on inventory size are further investigated. It is found that the optimal stockpile acquisition strategies are mainly determined by

Y. Bai; D. Q. Zhou; P. Zhou; L. B. Zhang

2012-01-01

252

An effective dynamic optimization method based on modified orthogonal collocation and reduced SQP

An effective dynamic optimization solution method based on the modified orthogonal collocation (mOC) and reduced successive quadratic programming (rSQP) is proposed, where the mOC is proposed to decrease the approximation error of the discrete optimal problem while traditional OC method converts the dynamic optimization problem to a regular but discrete nonlinear programming (NLP) problem, and the rSQP method is introduced

Xinggao Liu; Long Chen; Yunqing Hu

2011-01-01

253

A permutation-based dual genetic algorithm for dynamic optimization problems

Adaptation to dynamic optimization problems is currently receiving growing interest as one of the most important applications\\u000a of genetic algorithms. Inspired by dualism and dominance in nature, genetic algorithms with the dualism mechanism have been\\u000a applied for several dynamic problems with binary encoding. This paper investigates the idea of dualism for combinatorial optimization\\u000a problems in dynamic environments, which are also

Lili Liu; Dingwei Wang; W. H. Ip

2009-01-01

254

NASA Astrophysics Data System (ADS)

The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.

Bulgakov, V. K.; Strigunov, V. V.

2009-05-01

255

Conceptualizing a tool to optimize therapy based on dynamic heterogeneity

NASA Astrophysics Data System (ADS)

Complex biological systems often display a randomness paralleled in processes studied in fundamental physics. This simple stochasticity emerges owing to the complexity of the system and underlies a fundamental aspect of biology called phenotypic stochasticity. Ongoing stochastic fluctuations in phenotype at the single-unit level can contribute to two emergent population phenotypes. Phenotypic stochasticity not only generates heterogeneity within a cell population, but also allows reversible transitions back and forth between multiple states. This phenotypic interconversion tends to restore a population to a previous composition after that population has been depleted of specific members. We call this tendency homeostatic heterogeneity. These concepts of dynamic heterogeneity can be applied to populations composed of molecules, cells, individuals, etc. Here we discuss the concept that phenotypic stochasticity both underlies the generation of heterogeneity within a cell population and can be used to control population composition, contributing, in particular, to both the ongoing emergence of drug resistance and an opportunity for depleting drug-resistant cells. Using notions of both ‘large’ and ‘small’ numbers of biomolecular components, we rationalize our use of Markov processes to model the generation and eradication of drug-resistant cells. Using these insights, we have developed a graphical tool, called a metronomogram, that we propose will allow us to optimize dosing frequencies and total course durations for clinical benefit. The authors dedicate this paper to Dr Barton Kamen who inspired its initiation and enthusiastically supported its pursuit.

Liao, David; Estévez-Salmerón, Luis; Tlsty, Thea D.

2012-12-01

256

Conceptualizing a Tool to Optimize Therapy Based on Dynamic Heterogeneity

Complex biological systems often display a randomness paralleled in processes studied in fundamental physics. This simple stochasticity emerges owing to the complexity of the system and underlies a fundamental aspect of biology called phenotypic stochasticity. Ongoing stochastic fluctuations in phenotype at the single-unit level can contribute to two emergent population phenotypes. Phenotypic stochasticity not only generates heterogeneity within a cell population, but also allows reversible transitions back and forth between multiple states. This phenotypic interconversion tends to restore a population to a previous composition after that population has been depleted of specific members. We call this tendency homeostatic heterogeneity. These concepts of dynamic heterogeneity can be applied to populations composed of molecules, cells, individuals, etc. Here we discuss the concept that phenotypic stochasticity both underlies the generation of heterogeneity within a cell population and can be used to control population composition, contributing, in particular, to both the ongoing emergence of drug resistance and an opportunity for depleting drug-resistant cells. Using notions of both “large” and “small” numbers of biomolecular components, we rationalize our use of Markov processes to model the generation and eradication of drug-resistant cells. Using these insights, we have developed a graphical tool, called a metronomogram, that we propose will allow us to optimize dosing frequencies and total course durations for clinical benefit. PMID:23197078

Liao, David; Estevez-Salmeron, Luis; Tlsty, Thea D.

2012-01-01

257

Photocathode Optimization for a Dynamic Transmission Electron Microscope: Final Report

The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.

Ellis, P; Flom, Z; Heinselman, K; Nguyen, T; Tung, S; Haskell, R; Reed, B W; LaGrange, T

2011-08-04

258

In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM can not only predict the city's future waste generation amount, but also reflect dynamic, interactive, and uncertain characteristics of the MSW management system. Four kernel functions such as linear kernel, polynomial kernel, radial basis function, and multi-layer perception kernel are chosen based on three quantitative simulation performance criteria [i.e. prediction accuracy (PA), fitting accuracy (FA) and over all accuracy (OA)]. The SVR with polynomial kernel has accurate prediction performance for MSW generation rate, with all of the three quantitative simulation performance criteria being over 96%. Two cases are considered based on different waste management policies. The results are valuable for supporting the adjustment of the existing waste-allocation patterns to raise the city's waste diversion rate, as well as the capacity planning of waste management system to satisfy the city's increasing waste treatment/disposal demands. PMID:21872384

Dai, C; Li, Y P; Huang, G H

2011-12-01

259

Optimal path planning is a key problem for the control of autonomous unmanned ground vehicles. Particle swarm optimization has been used to solve the optimal problem in the static environment; however, optimal path planning for UGV groups in a dynamical environment has not been fully discussed. Accordingly, a dynamic obstacle-avoidance path planning for an unmanned ground vehicle group was considered

Yunji Wang; Philip Chen; Yufang Jin

2009-01-01

260

Metamodeling and the Critic-based approach to multi-level optimization.

Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. PMID:22386785

Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J

2012-08-01

261

Optimized dynamic framing for PET-based myocardial blood flow estimation

NASA Astrophysics Data System (ADS)

An optimal experiment design methodology was developed to select the framing schedule to be used in dynamic positron emission tomography (PET) for estimation of myocardial blood flow using 82Rb. A compartment model and an arterial input function based on measured data were used to calculate a D-optimality criterion for a wide range of candidate framing schedules. To validate the optimality calculation, noisy time-activity curves were simulated, from which parameter values were estimated using an efficient and robust decomposition of the estimation problem. D-optimized schedules improved estimate precision compared to non-optimized schedules, including previously published schedules. To assess robustness, a range of physiologic conditions were simulated. Schedules that were optimal for one condition were nearly-optimal for others. The effect of infusion duration was investigated. Optimality was better for shorter than for longer tracer infusion durations, with the optimal schedule for the shortest infusion duration being nearly optimal for other durations. Together this suggests that a framing schedule optimized for one set of conditions will also work well for others and it is not necessary to use different schedules for different infusion durations or for rest and stress studies. The method for optimizing schedules is general and could be applied in other dynamic PET imaging studies.

Kolthammer, Jeffrey A.; Muzic, Raymond F.

2013-08-01

262

An optimization model for energy generation and distribution in a dynamic facility

NASA Technical Reports Server (NTRS)

An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.

Lansing, F. L.

1981-01-01

263

Global Optimization using a Dynamical Systems Stefan Sertl and Michael Dellnitz

Global Optimization using a Dynamical Systems Approach Stefan Sertl and Michael Dellnitz Faculty. Similar approaches can be found e.g. in Hsu (1987) or Osipenko and Komarchev (1995). Already in Dellnitz

Neumaier, Arnold

264

Global Optimization using a Dynamical Systems Stefan Sertl and Michael Dellnitz

Global Optimization using a Dynamical Systems Approach Stefan Sertl and Michael Dellnitz Faculty.g. in Hsu (1987) or Osipenko and Komarchev (1995). Already in Dellnitz et al. (2002) subdivision techniques

265

Technology Advances for Dynamic Real-Time Optimization

Integration of real-time optimization and control is an essential task for profitable process operation in a highly competitive environment. While integrated large-scale optimization models have been formulated for this task, their size and complexity is often a challenge to many available optimization solvers. On the other hand, recent development of powerful, large-scale solvers leads to a reconsideration of these formulations,

L. T. Biegler

2009-01-01

266

The Emergence of Optimal Agglomeration in Dynamic Economics

We analyze endogenous pattern formation resulting from forward-looking optimizing behavior of economic agents in the presence of spatial spillovers modelled by continuous kernels. We use Fourier methods to identify nec- essary and suÃƒï¿½Ã‚Â¢ cient conditions for the emergence of optimal agglomeration through an optimal spillover induced instability of a spatially homogeneous steady state. We apply our methods to study the

William Brock; Anastasios Xepapadeas

267

Interim monitoring of clinical trials: decision theory, dynamic programming and optimal stopping

Interim monitoring of clinical trials: decision theory, dynamic programming and optimal stopping.S.A ABSTRACT It is standard practice to monitor clinical trials with a view to stopping early if results multipliers. Applications of these methods in clinical trial design include the derivation of optimal adaptive

Budd, Chris

268

Dynamic Optimization under Uncertainty via NCO Tracking: A Solution Model Approach

The use of measurements to compensate the effect of uncertainty has recently gained atten- tion in the context of optimization of dynamic systems. In this field, termed measurement-based optimization, two main categories can be distinguished depending on whether a model of the process or a model of the solution is used for processing the measurements. The former has been studied

B. Srinivasan; D. Bonvin

269

Selection of prestress for optimal dynamic\\/control performance of tensegrity structures

This paper concerns prestress optimization of a tensegrity structure for its optimal LQR performance. A linearized dynamic model of the structure is derived in which the force-density variables that parameterize the prestress of the structure appear linearly. A feasible region for these parameters is defined in terms of the extreme directions of the prestress cone. A numerical method for computing

Milenko Masic; Robert E. Skelton

2006-01-01

270

In this paper, A modified intelligent Particle Swarm Optimization (PSO) and continuous Genetic Algorithms (GA) have been used for optimal selection of the static synchronous series compensator (SSSC) damping controller parameters in order to improve power system dynamic response and its stability. Then the performance of these methods on system stability has been compared. First intelligent PSO and genetic algorithms

S. Khani; M. Sadeghi; S. H. Hosseini

2010-01-01

271

A comprehensive computer program is designed in MATLAB to analyze, design and optimize the propulsion, dynamics, thermodynamics, and kinematics of any serial multi-staging rocket for a set of given data. The program is quite user-friendly. It comprises two main sections: ``analysis and design'' and ``optimization.'' Each section has a GUI (Graphical User Interface) in which the rocket's data are entered

Mehdi Lali

2009-01-01

272

Large-scale dynamic optimization for grade transitions in a low density polyethylene plant

This paper presents the optimal control policy of an industrial low-density polyethylene (LDPE) plant. Based on a dynamic model of the whole plant, optimal feed profiles are determined to minimize the transient states generated during the switching between different steady states. The industrial process under study produces LDPE by high-pressure polymerization of ethylene in a tubular reactor using oxygen and

A. M. Cervantes; S. Tonelli; A. Brandolin; J. A. Bandoni; L. T. Biegler

2002-01-01

273

Large-scale dynamic optimization of a low density polyethylene plant

This paper presents the optimal control policy of an industrial low-density polyethylene (LDPE) plant. Based on a dynamic model of the whole plant, optimal feed profiles are determined to minimize the transient states generated during the switching between different steady states. This industrial process produces LDPE by high-pressure polymerization of ethylene in a tubular reactor. The plant produces different final

A. Cervantes; S. Tonelli; A. Brandolin; A. Bandoni; L. Biegler

2000-01-01

274

Optimal control of coupled spin dynamics in the presence of relaxation

In this thesis, we study methods for optimal manipulation of coupled spin dynamics in the presence of relaxation. We use these methods to compute analytical upper bounds for the efficiency of coherence and polarization transfer between coupled nuclear spins in multidimensional nuclear magnetic resonance (NMR) experiments, under the presence of relaxation. We derive relaxation optimized pulse sequences which achieve or

Dionisis Stefanatos

2005-01-01

275

Optimizing Dynamic Programming on Graphics Processing Units via Adaptive Thread-Level Parallelism

Dynamic programming (DP) is an important computational method for solving a wide variety of discrete optimization problems such as scheduling, string editing, packaging, and inventory management. In general, DP is classified into four categories based on the characteristics of the optimization equation. Because applications that are classified in the same category of DP have similar program behavior, the research community

Chao-Chin Wu; Jenn-Yang Ke; Heshan Lin; Wu-chun Feng

2011-01-01

276

Sequential design of dynamic experiments in modeling for optimization of biological processes

Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. A methodology for model-based design of dynamic experiments in modeling for optimization is proposed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 whose DNA has

Mariano Cristaldi; Ricardo Grau; Ernesto Martínez

2009-01-01

277

Optimal GENCO bidding strategy

NASA Astrophysics Data System (ADS)

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.

Gao, Feng

278

A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180

Wang, Jian; Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping

2014-01-01

279

Representations for optimal stopping under dynamic monetary utility functionals

dynamic risk assessment of financial positions should allow for updating as time evolves, taking becomes a dynamic risk measure (e.g. in [21]) which represents preferences in terms of losses instead to the study of dynamic risk measures which became an increasing research field in the last years. A realistic

Schoenmakers, John

280

Phase balancing optimization for radial feeder systems

NASA Astrophysics Data System (ADS)

In the power distribution systems, unbalanced feeder causes deteriorating power quality and increases investment and operating costs. There are two approaches to balance a feeder system. One approach is feeder reconfiguration, the other is phase swapping. Feeder reconfiguration is an on-line operation of sectionalizing switches. However, feeder reconfiguration is difficult to find the phase balancing solution for a unbalanced feeder system. On the other hand, phase swapping is a direct and effective approach for phase balancing. Phase swapping is an off-line operation of re-tapping loads/laterals to the phase lines during maintenance and restoration periods. Unfortunately, phase balancing problem has not received its deserved importance. Deregulation in power industry has arisen phase balancing issue because it can improve power quality, improve service reliability, and reduce total costs. Thus, phase balancing can enhance utility competitive capability. This research provides utilities the optimization tools to maximize the benefits of phase balancing and minimize the costs. This dissertation proposed several mathematical formulations, including Mixed-integer programming (MIP), Dynamic programming (DP), Simulated annealing (SA), and Fuzzy logic (FL), to perform phase swapping to balance phase in a radial feeder. Due to the discrete nature of phase swapping, a MIP model is proposed to find the optimal phase swapping scheme for small-sized feeder. Candidate sets and monitored branches are then introduced to solve the large-scale feeder systems. Possibilistic integer programming (PIP) is proposed to incorporate load uncertainties in phase swapping problems. The tests show that the load uncertainties may change the optimal phase swapping. DP computation is significant reduced by introducing the tighter upper/lower bounds. The upper bound can be quickly obtained by assignment algorithm. Even though SA is a time-consuming heuristic method, it can provide better solution than other heuristic methods. FL can efficiently identify the most effective phase swapping for large-scale feeder systems. The results can be easily fine-tuned to match the expectation of decision-maker. Finally, the computation efforts and optimality are compared between the proposed methods. Several examples are used to illustrate the effectiveness of the proposed methods.

Zhu, Jinxiang

281

An archived multi-objective simulated annealing for a dynamic cellular manufacturing system

NASA Astrophysics Data System (ADS)

To design a group layout of a cellular manufacturing system (CMS) in a dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three interrelated decisions involved in the design of a CMS. This paper provides an extensive coverage of important manufacturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctuations of part demands more economically by adding machine depot and PP decisions. Two conflicting objectives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operators are the advantages of the designed AMOSA. To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., ?-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA-II for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.

Shirazi, Hossein; Kia, Reza; Javadian, Nikbakhsh; Tavakkoli-Moghaddam, Reza

2014-05-01

282

Towards Dynamic Pricing-Based Collaborative Optimizations for Green Data Centers

using real intermittent-energy-generation trace data. Modeling the dynamic price over this trace, weTowards Dynamic Pricing-Based Collaborative Optimizations for Green Data Centers Yang Li David Chiu. Meanwhile, market penetration of intermittent renewable energy sources (e.g., wind and solar) is on the rise

Loo, Boon Thau

283

Optimized Query Planning of Continuous Aggregation Queries in Dynamic Data Dissemination Networks

propose a method to efficiently answer aggregation queries involving such data items. In dataOptimized Query Planning of Continuous Aggregation Queries in Dynamic Data Dissemination Networks-cost, scalable technique to answer continuous aggregation queries using a content distribution network of dynamic

Ramamritham, Krithi

284

Gain-scheduled `1 -optimal control for boiler-turbine dynamics

Gain-scheduled `1 -optimal control for boiler-turbine dynamics with actuator saturation Pang; accepted 2 June 2003 Abstract This paper presents a gain-scheduled approach for boiler-turbine controller the magnitude and rate saturation constraints on actuators. The nonlinear boiler-turbine dynamics is brought

Shamma, Jeff S.

285

Greedy Algorithms In dynamic programming, the optimal solution is described in a recursive manner,

CHAPTER 16 Greedy Algorithms Â· In dynamic programming, the optimal solution is described an alternative design technique, called greedy algorithms. Â· This method typically leads to simpler and faster algorithms, but it is not as powerful or as widely applicable as dynamic programming. Â· The greedy concept

Dragan, Feodor F.

286

Topology and Dynamic Networks: Optimization with Application in Future Technologies

\\u000a The optimal design and control of infrastructures, e.g. in traffic control, water-supply, sewer-systems and gas-pipelines,\\u000a the optimization of structures, form and formation of materials, e.g. in lightweight structures, play a predominant role in\\u000a modern fundamental and applied research. However, until very recently, simulation-based optimization has been employed in\\u000a the sense that parameters are being adjusted in a forward simulation using

Günter Leugering; Alexander Martin; Michael Stingl

287

SIMPLE IMPLEMENTATION OF DYNAMIC OPTIMAL Sridharakumar Narasimhan , Sigurd Skogestad ,1

definition of simplicity is presented for a certain class of systems. Some Heat Exchanger Network problems and noise on the fast time scales. This is the concept of self-optimizing control where we try to achieve

Skogestad, Sigurd

288

Adaptive Robust Optimization with Dynamic Uncertainty Sets for ...

Sep 9, 2014 ... [6] presents a short-term forward electricity market-clearing model ... current operational practice; robust optimization provides a data-driven way to ..... the dispatch solution, which will be implemented right away for time t = 1.

2014-09-29

289

NASA Technical Reports Server (NTRS)

This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general-purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of global quantities (stiffness, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.

Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.

1995-01-01

290

Triggered Memory-Based Swarm Optimization in Dynamic Environments

problem shows that the triggered memory scheme efficiently improves the performance of PSOs in dynamic as follows. The next section briefly describes the PSO and surveys the literature on PSOs for DOPs. Section 3 proposes a variety of triggered memory-based methods for PSOs to handle dynamic envi- ronments. Then

Yang, Shengxiang

291

Optimized Dynamical Decoupling in ?-Type n-Level Quantum Systems

In this paper, we first design a type of Bang-Bang (BB) operation group to reduce the phase decoherence in a {\\Xi}-type n-level quantum system based on the dynamical decoupling mechanism. Then, we derive two kinds of dynamical decoupling schemes: periodic dynamical decoupling (PDD) and Uhrig dynamical decoupling (UDD). We select the non-diagonal element of density matrix as a reference index, and investigate the behavior of quantum coherence of the {\\Xi}-type n-level atom under these two dynamical decoupling schemes proposed. At last, we choose a {\\Xi}-type six-level atom as a system controlled, and use the decoupling schemes proposed to suppress the phase decoherence. The simulation experiments and the comparison results are given.

Linping Chan; Shuang Cong

2014-02-21

292

Optimal buffer size and dynamic rate control for a queueing network with impatient customers abandonment in heavy traffic. The controller can choose a buffer size for the queuing network and also can an asymptotically optimal control policy, i.e. an optimal buffer size and an optimal service rate for the queueing

Ghosh, Arka P.

293

NASA Astrophysics Data System (ADS)

Navigation Satellite System (GNSS)-based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium-range Weather Forecasts (ECMWF) short-range forecast and analysis fields, together with averaged-observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Wu, S.; Schwaerz, M.; Fritzer, J.; Zhang, S.; Carter, B. A.; Zhang, K.

2013-12-01

294

Dynamic Rate Control Algorithms for HDR Throughput Optimization

The relative delay tolerance of data applications, together with the bursty traffic character- istics, opens up the possibility for scheduling transmissions so as to optimize throughput. A particularly attractive approach, in fading environments, is to exploit the variations in the channel conditions, and transmit to the user with the currently 'best' channel. We show that the 'best' user may be

Sem Borst; Phil Whiting

295

Constrained Molecular Dynamics as a Search and Optimization Tool

partly inspired by) particle swarm optimization (PSO) [5]. Like our system, PSOs use groups of interacting par- ticles. In PSOs such particles fly over the fitness landscape, recording the best places seen by PSOs. Our system differs from a PSO in many ways. Firstly, the motion of our particles is constrained

Poli, Riccardo

296

INTRAOPERATIVE DYNAMIC DOSE OPTIMIZATION IN PERMANENT PROSTATE IMPLANTS

Purpose: With the advent of intraoperative optimized planning, the treatment of prostate cancer with permanent implants has reached an unprecedented level of dose conformity. However, because of well-documented (and unavoidable) inaccuracies in seed placement into the gland, carrying out a plan results in a large degree of variability relative to the intended dose distribution. This brings forth the need to

EVA K. LEE; MARCO ZAIDER

2003-01-01

297

Characterization of control noise effects in optimal quantum unitary dynamics

The control of quantum mechanical unitary transformations naturally calls for a degree of resilience to control field noise. While consideration of noise effects in quantum systems has been an area of active study, the relationship between optimal solutions and those that are both optimal and robust to noise is still not generally understood. This work defines measures for quantifying the effects of field noise upon targeted unitary transformations. Robustness to noise is assessed in the framework of the quantum control landscape, which is the mapping from the control to the unitary transformation performance measure. Within that framework, more robust optimal controls are associated with regions of low landscape curvature. The utility of this perspective when considering the effects of noise is demonstrated through numerical simulations of the overlap between directions of significant curvature on the landscape and noise correlation functions. These simulations demonstrate both the rich and varied nature of optimal and robust controls, as well as reveal distinct noise spectral regimes that support robust control solutions for a class of transformations considered in quantum information processing.

David Hocker; Constantin Brif; Matthew D. Grace; Ashley Donovan; Tak-San Ho; Katharine W. Moore Tibbetts; Rebing Wu; Herschel Rabitz

2014-05-23

298

Dynamic network flow optimization models for air vehicle resource allocation

A weapon system consisting of a swarm of air vehicles whose mission is to search for, classify, attack, and perform battle damage assessment, is considered. It is assumed that the target field information is communicated to all the elements of the swarm as it becomes available. A network flow optimization problem is posed whose readily obtained solution yields the optimum

Kendall E. Nygard; Phillip R. Chandler; M. Pachter

2001-01-01

299

Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model

Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide future search, but there is no common successful method to solve different DMOPs. In this paper, we define a kind of dynamic multiobjectives problem with translational Paretooptimal set (DMOP-TPS) and propose a new prediction model named ADLM for solving DMOP-TPS. We have tested and compared the proposed prediction model (ADLM) with three traditional prediction models on several classic DMOP-TPS test problems. The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS. PMID:24616625

Xie, Zhaoxin; Chen, Chao; Sallam, Ahmed

2014-01-01

300

NASA Astrophysics Data System (ADS)

A molecular dynamics calculation of the amino acid polar requirement is used to score the canonical genetic code. Monte Carlo simulation shows that this computational polar requirement has been optimized by the canonical genetic code, an order of magnitude more than any previously known measure, effectively ruling out a vertical evolution dynamics. The sensitivity of the optimization to the precise metric used in code scoring is consistent with code evolution having proceeded through the communal dynamics of statistical proteins using horizontal gene transfer, as recently proposed. The extreme optimization of the genetic code therefore strongly supports the idea that the genetic code evolved from a communal state of life prior to the last universal common ancestor.

Butler, Thomas; Goldenfeld, Nigel; Mathew, Damien; Luthey-Schulten, Zaida

2009-06-01

301

INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization

It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.

Groer, Christopher S [ORNL; Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL

2012-10-01

302

NASA Astrophysics Data System (ADS)

The possibility of applying the method of integral manifolds to the reduction of optimal filtering problems for systems with low energy dissipation is explored. For such systems, it is shown that the slow subsystem of matrix Riccati differential equations turns out to have a higher dimension than expected, which leads to an increase in the dimension of the reduced problems. An optimal filter is constructed for the Langevin equation and for a dynamic model of a single-link flexible manipulator.

Osintsev, M. S.; Sobolev, V. A.

2014-01-01

303

Integration of dynamic, aerodynamic, and structural optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

Summarized here is the first six years of research into the integration of structural, dynamic, and aerodynamic considerations in the design-optimization process for rotor blades. Specifically discussed here is the application of design optimization techniques for helicopter rotor blades. The reduction of vibratory shears and moments at the blade root, aeroelastic stability of the rotor, optimum airframe design, and an efficient procedure for calculating system sensitivities with respect to the design variables used are discussed.

Peters, David A.

1991-01-01

304

\\u000a A new fuzzy modeling method using Multi-population Cooperative Particle Swarm Optimizer (MCPSO) for identification and control\\u000a of nonlinear dynamic systems is presented in this paper. In MCPSO, the population consists of one master swarm and several\\u000a slave swarms. The slave swarms executeParticle Swarm Optimization (PSO) or its variants independently to maintain the diversity\\u000a of particles, while the particles in the

Ben Niu; Yunlong Zhu; Xiaoxian He

2005-01-01

305

Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles

NASA Technical Reports Server (NTRS)

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Morelli, Eugene A.; Klein, Vladislav

1990-01-01

306

Optimal input design for aircraft parameter estimation using dynamic programming principles

NASA Technical Reports Server (NTRS)

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Klein, Vladislav; Morelli, Eugene A.

1990-01-01

307

NASA Astrophysics Data System (ADS)

A numerical algorithm based on forward dynamic programming and a branch-and-bound scheme, developed by Hanaoka and Tanabe (1982) to solve nonlinear state-constrained optimal-control problems, is applied to the optimization of low-thrust trajectories for space missions. The derivation and basic features of the algorithm are reviewed, and numerical results for a solar-encounter mission with the option of a single Venus swingby are presented graphically. The algorithm is found to give accurate results with good convergence; the computation time and memory requirements are significantly lower than those of conventional dynamic-programming methods.

Hanaoka, Teruaki

308

Control Design for Autonomous Vehicles: A Dynamic Optimization Perspective

Control design for autonomous vehicles involves a number of issues that are not satisfactorily addressed in classical control systems theory. There is typically the need for prescribing and commanding a collection of interacting dynamic control systems in order to meet the desired requirements for overall behavior, whereas conventional control design has only one system to govern. This context requires a

Fernando Lobo Pereira

2001-01-01

309

Bath optimization in the cellular dynamical mean-field theory

In the cellular dynamical mean-field theory (CDMFT), a strongly correlated system is represented by a small cluster of correlated sites, coupled to an adjustable bath of uncorrelated sites simulating the cluster's environment; the parameters governing the bath are set by a self-consistency condition involving the local Green's function and the lattice electron dispersion. Solving the cluster problem with an exact

David Sénéchal

2010-01-01

310

Seizure warning algorithm based on optimization and nonlinear dynamics

There is growing evidence that temporal lobe seizures are preceded by a preictal transition, characterized by a gradual dynamical change from asymptomatic interictal state to seizure. We herein report the first prospective analysis of the online automated algorithm for detecting the preictal transition in ongoing EEG signals. Such, the algorithm constitutes a seizure warning system. The algorithm estimates STL max,

Panos M. Pardalos; Wanpracha Art Chaovalitwongse; Leonidas D. Iasemidis; J. Chris Sackellares; Deng-shan Shiau; Paul R. Carney; Oleg A. Prokopyev; Vitaliy A. Yatsenko

2004-01-01

311

Augmented LQG Optimal Control of Dynamic Performance for ETG System

Running of unstable speed exhaust turbine generator (ETG) using main engine (ME) waste heat by high turbulence intensities, in order to demand maximization of regenerative energy harvested from the ME waste heat and minimization of damage caused by mechanical losses and fatigue. ALQG control scheme for output power leveling with unknown dynamics is proposed in this paper. The control scheme

Gui-chen Zhang

2009-01-01

312

A new method of optimal design for a two-dimensional diffuser by using dynamic programming

NASA Technical Reports Server (NTRS)

A new method for predicting the optimal velocity distribution on the wall of a two dimensional diffuser is presented. The method uses dynamic programming to solve the optimal control problem with inequality constraints of state variables. The physical model of optimization is designed to prevent the separation of the boundary layer while approaching the maximum pressure ratio in a diffuser of a specified length. The computational results are in fair agreement with the experimental ones. Optimal velocity distribution on a diffuser wall is said to occur when the flow decelerates quickly at first and then smoothly, while the flow is near separation, but always protected from it. The optimal velocity distribution can be used to design the contour of the diffuser.

Gu, Chuangang; Zhang, Moujin; Chen, XI; Miao, Yongmiao

1991-01-01

313

System design optimization for stand-alone photovoltaic systems sizing by using superstructure model

NASA Astrophysics Data System (ADS)

Although the photovoltaic (PV) systems have been increasingly installed as an alternative and renewable green power generation, the initial set up cost, maintenance cost and equipment mismatch are some of the key issues that slows down the installation in small household. This paper presents the design optimization of stand-alone photovoltaic systems using superstructure model where all possible types of technology of the equipment are captured and life cycle cost analysis is formulated as a mixed integer programming (MIP). A model for investment planning of power generation and long-term decision model are developed in order to help the system engineer to build a cost effective system.

Azau, M. A. M.; Jaafar, S.; Samsudin, K.

2013-06-01

314

Dynamic simulation and optimal control strategy of a decentralized supply chain system

Efficient management of inventory in supply chains is critical to the profitable operation of modern enterprises. The supply\\/demand networks characteristic of discrete-parts industries represent highly stochastic, nonlinear, and constrained dynamical systems whose study merits a control-oriented approach. Minimum variance control (MVC) strategy is applied to solve the dynamic optimization problems of the inventory for a decentralized supply chain system. Transfer

Dong Hai; Li Yan-ping

2009-01-01

315

NASA Astrophysics Data System (ADS)

Recent contributions to the ecohydrological literature have questioned the continued usefulness of the classical model calibration paradigm to estimating parameters in coupled models of soil moisture dynamics, ecophysiological gas exchange and photosynthesis. In their seminal papers, (Kleidon, 2004; Kleidon, 2007; Schymanski et al., 2007; Schymanski et al., 2008a) have demonstrated that the principle of vegetation optimality provides an attractive and parsimonious alternative to using site specific calibration measurements for estimating vegetation cover, rooting depth, transpiration fluxes, and CO2 assimilation. Optimality based approaches not only reduce the need for detailed field measurements, but also provide flexible frameworks to continuously adapt vegetation responses to changing environmental conditions. Yet, the main focus in optimality-based approaches has been on finding a single optimal combination of ecohydrological parameter values. This approach downplays the importance of variability, and implicitly ignores model and measurement uncertainty. Here we show that significant advances to optimality based modelling can be made if we embrace a novel concept of stochastic optimization that includes explicit recognition of parameter uncertainty. To illustrate our ideas, we develop a multi-layer soil and canopy Vegetation Optimality Model, hereafter referred to as VOM(mlsc), and apply this extended VOM model to a Douglas Fir stand in the Netherlands. We use the DiffeRential Evolution Adaptive Metropolis (DREAM) Markov Chain Monte Carlo algorithm for parameter exploration with NCP as optimality criteria. Our results show that significant dispersion exists in optimized vegetation structure and properties from optimality of NCP, and that modelled and measured H2O and CO2 fluxes compare rather poorly. These findings question the usefulness of NCP as single optimality criteria, and advocate the simultaneous use of multiple non-commensurable (optimality) criteria for ecohydrological parameter estimation and model evaluation.

Dekker, S. C.; Elkington, R. J.; Vrugt, J. A.

2009-04-01

316

Dynamic programming algorithm optimization for spoken word recognition

This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm

HIROAKI SAKOE; SEIBI CHIBA

1978-01-01

317

Dynamics of ultrathin laser targets with optimal parameters

NASA Astrophysics Data System (ADS)

The set of equations describing the motion of a thin (compared to the wavelength) target in the field of a laser pulse that takes into consideration separate motion of the electron and ion layers is derived. In the case of strong Coulomb coupling between the layers, the set of equation of motions of the layers is reduced to the well-known light-sail equation containing a self-consistent coefficient of nonlinear reflection of laser radiation by a moving target. The optimal thickness of the laser target at which the target acquires maximum energy for given laser-pulse parameters is determined. It is shown that this thickness depends not only on laser intensity, but also on laser-pulse duration and the ratio of electron and ion masses. The growth rates of transverse instability of optimal targets under their intense acceleration are analyzed. It is demonstrated that instability does not develop in the currently experimentally accessible range of laser intensities and pulse durations between 100 and 200 fs.

Andreev, A. A.; Platonov, K. Yu.; Chestnov, V. I.; Petrov, A. E.

2014-08-01

318

A stable elemental decomposition for dynamic process optimization

NASA Astrophysics Data System (ADS)

In Cervantes and Biegler (A.I.Ch.E.J. 44 (1998) 1038), we presented a simultaneous nonlinear programming problem (NLP) formulation for the solution of DAE optimization problems. Here, by applying collocation on finite elements, the DAE system is transformed into a nonlinear system. The resulting optimization problem, in which the element placement is fixed, is solved using a reduced space successive quadratic programming (rSQP) algorithm. The space is partitioned into range and null spaces. This partitioning is performed by choosing a pivot sequence for an LU factorization with partial pivoting which allows us to detect unstable modes in the DAE system. The system is stabilized without imposing new boundary conditions. The decomposition of the range space can be performed in a single step by exploiting the overall sparsity of the collocation matrix but not its almost block diagonal structure. In order to solve larger problems a new decomposition approach and a new method for constructing the quadratic programming (QP) subproblem are presented in this work. The decomposition of the collocation matrix is now performed element by element, thus reducing the storage requirements and the computational effort. Under this scheme, the unstable modes are considered in each element and a range-space move is constructed sequentially based on decomposition in each element. This new decomposition improves the efficiency of our previous approach and at the same time preserves its stability. The performance of the algorithm is tested on several examples. Finally, some future directions for research are discussed.

Cervantes, Arturo M.; Biegler, Lorenz T.

2000-08-01

319

Improving the dynamic characteristics of body-in-white structure using structural optimization.

The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312

Yahaya Rashid, Aizzat S; Ramli, Rahizar; Mohamed Haris, Sallehuddin; Alias, Anuar

2014-01-01

320

Improving the Dynamic Characteristics of Body-in-White Structure Using Structural Optimization

The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312

Yahaya Rashid, Aizzat S.; Mohamed Haris, Sallehuddin; Alias, Anuar

2014-01-01

321

This paper presents an optimized envelope tracking (ET) operation of a Doherty amplifier. Compared to the general ET\\/envelope elimination and restoration transmitter, it has an advantage of the extended dynamic range of 6 dB for the load modulation of a Doherty amplifier. Moreover, by modulating the supply voltage of the carrier amplifier, while that of the peaking amplifier is fixed,

Jinsung Choi; Daehyun Kang; Dongsu Kim; Bumman Kim

2009-01-01

322

An optimality-based model of the dynamic feedbacks between natural vegetation and the water balance

The hypothesis that vegetation adapts optimally to its environment gives rise to a novel framework for modeling the interactions between vegetation dynamics and the catchment water balance that does not rely on prior knowledge about the vegetation at a particular site. We present a new model based on this framework that includes a multilayered physically based catchment water balance model

S. J. Schymanski; M. Sivapalan; M. L. Roderick; L. B. Hutley; J. Beringer

2009-01-01

323

Kinodynamic RRT*: Asymptotically Optimal Motion Planning for Robots with Linear Dynamics

with a 10-D state space and linearized quadrotor dynamics, and (iii) a car-like robot with a 5-D state space@cs.utah.edu. Fig. 1. An asymptotically optimal trajectory computed by our algorithm for a quadrotor helicopter

van den Berg, Jur

324

China's Strategic Petroleum Reserve (SPR) is currently being prepared. But how large the optimal stockpile size for China should be, what the best acquisition strategies are, how to release the reserve if a disruption occurs, and other related issues still need to be studied in detail. In this paper, we develop a stochastic dynamic programming model based on a total

Xiao-Bing Zhang; Ying Fan; Yi-Ming Wei

2009-01-01

325

Optimization of Fed-Batch Saccharomyces cereWisiae Fermentation Using Dynamic Flux Balance Models

is that nutrient levels can be varied to achieve favorable growth conditions. Fed-batch yeast fermentation-batch yeast fermenters have been extensively investigated (10-15). These studies were based on simpleARTICLES Optimization of Fed-Batch Saccharomyces cereWisiae Fermentation Using Dynamic Flux Balance

Mountziaris, T. J.

326

A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware

A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware Computing Vasanth and longer lasting, but battery technology is not improving at the same rate that power consumption is rising. Traditional power-management research is fragmented; techniques are being developed at specific levels

327

Optimization and the Price of Anarchy in a Dynamic Newsboy Model

This paper examines a dynamic version of the newsboy problem in which a decision maker must maintain service capacity from several sources to attempt to meet uncertain demand for a perishable good, subject to the cost of providing sufficient capacity, and penalties for not meeting demand. A complete characterization of the optimal outcome is obtained when normalized demand is modeled

In-Koo Cho; Sean P. Meyn

328

Time limited optimal dynamics beyond the Quantum Speed Limit

The quantum speed limit sets the minimum time required to transfer a quantum system completely into a given target state. At shorter times the higher operation speed has to be paid with a loss of fidelity. Here we quantify the trade-off between the fidelity and the duration in a system driven by a time-varying control and interpret the result in Hilbert space geometry. Formulating a necessary convergence criterion for Optimal Control (OC) algorithms allows us to implement an algorithm which minimizes the process duration while obtaining a predefined fidelity. The algorithm is demonstrated for a multilevel system with a constrained Hamiltonian, and a classification scheme for the control sequences is proposed based on their optimizability.

Miroslav Gajdacz; Kunal K. Das; Jan Arlt; Jacob F. Sherson; Tomáš Opatrný

2014-05-23

329

Locusts use dynamic thermoregulatory behaviour to optimize nutritional outcomes

Because key nutritional processes differ in their thermal optima, ectotherms may use temperature selection to optimize performance in changing nutritional environments. Such behaviour would be especially advantageous to small terrestrial animals, which have low thermal inertia and often have access to a wide range of environmental temperatures over small distances. Using the locust, Locusta migratoria, we have demonstrated a direct link between nutritional state and thermoregulatory behaviour. When faced with chronic restrictions to the supply of nutrients, locusts selected increasingly lower temperatures within a gradient, thereby maximizing nutrient use efficiency at the cost of slower growth. Over the shorter term, when locusts were unable to find a meal in the normal course of ad libitum feeding, they immediately adjusted their thermoregulatory behaviour, selecting a lower temperature at which assimilation efficiency was maximal. Thus, locusts use fine scale patterns of movement and temperature selection to adjust for reduced nutrient supply and thereby ameliorate associated life-history consequences. PMID:21288941

Coggan, Nicole; Clissold, Fiona J.; Simpson, Stephen J.

2011-01-01

330

Scaling and optimization of the radiation temperature in dynamic hohlraums

The authors have constructed a quasi-analytic model of the dynamic hohlraum. Solutions only require a numerical root solve, which can be done very quickly. Results of the model are compared to both experiments and full numerical simulations with good agreement. The computational simplicity of the model allows one to find the behavior of the hohlraum temperature as a function the various parameters of the system and thus find optimum parameters as a function of the driving current. The model is used to investigate the benefits of ablative standoff and axial convergence.

SLUTZ,STEPHEN A.; DOUGLAS,MELISSA R.; LASH,JOEL S.; VESEY,ROGER A.; CHANDLER,GORDON A.; NASH,THOMAS J.; DERZON,MARK S.

2000-04-13

331

NASA Astrophysics Data System (ADS)

In recent years, multi-modal optimization has become an important area of active research. Many algorithms have been developed in literature to tackle multi-modal optimization problems. In this work, a dynamic grouping crowding differential evolution (DGCDE) with ensemble of parameter is proposed. In this algorithm, the population is dynamically regrouped into 3 equal subpopulations every few generations. Each of the subpopulations is assigned a set of parameters. The algorithms is tested on 12 classical benchmark multi-modal optimization problems and compared with the crowding differential evolution (Crowding DE) in literature. As shown in the experimental results, the proposed algorithm outperforms the Crowding DE with all three different parameter settings on the benchmark problems.

Qu, Bo Yang; Gouthanan, Pushpan; Suganthan, Ponnuthurai Nagaratnam

332

Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming

. Learning to do the right thing at each instant in situations that evolve over time is di cult functions are di cult to learn directly, but they can be built up from learned models of the dynamics by the value function. u = argminu (L(x; u)+ V (f(x; u))) (1) Value functions are di cult to learn

333

Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of the multibody dynamics discipline make it conducive these days for other types of applications, in addition to pure simulations. One very important such application is design optimization. A very important first step towards design optimization is sensitivity analysis of multibody system dynamics. Dynamic sensitivities are often calculated by means of finite differences. Depending of the number of parameters involved, this procedure can be computationally expensive. Moreover, in many cases, the results suffer from low accuracy when real perturbations are used. The main contribution to the state-of-the-art brought by this study is the development of the adjoint sensitivity approach of multibody systems in the context of the penalty formulation. The theory developed is demonstrated on one academic case study, a five-bar mechanism, and on one real-life system, a 14-DOF vehicle model. The five-bar mechanism is used to illustrate the sensitivity approach derived in this paper. The full vehicle model is used to demonstrate the capability of the new approach developed to perform sensitivity analysis and gradient-based optimization for large and complex multibody systems with respect to multiple design parameters.

Yitao Zhu; Daniel Dopico; Corina Sandu; Adrian Sandu

2014-10-30

334

Optimization of rotor blades for combined structural, dynamic, and aerodynamic properties

NASA Technical Reports Server (NTRS)

Optimal helicopter blade design with computer-based mathematical programming has received more and more attention in recent years. Most of the research has focused on optimum dynamic characteristics of rotor blades to reduce vehicle vibration. There is also work on optimization of aerodynamic performance and on composite structural design. This research has greatly increased our understanding of helicopter optimum design in each of these aspects. Helicopter design is an inherently multidisciplinary process involving strong interactions among various disciplines which can appropriately include aerodynamics; dynamics, both flight dynamics and structural dynamics; aeroelasticity: vibrations and stability; and even acoustics. Therefore, the helicopter design process must satisfy manifold requirements related to the aforementioned diverse disciplines. In our present work, we attempt to combine several of these important effects in a unified manner. First, we design a blade with optimum aerodynamic performance by proper layout of blade planform and spanwise twist. Second, the blade is designed to have natural frequencies that are placed away from integer multiples of the rotor speed for a good dynamic characteristics. Third, the structure is made as light as possible with sufficient rotational inertia to allow for autorotational landing, with safe stress margins and flight fatigue life at each cross-section, and with aeroelastical stability and low vibrations. Finally, a unified optimization refines the solution.

He, Cheng-Jian; Peters, David A.

1990-01-01

335

NASA Technical Reports Server (NTRS)

A challenge for the fluid dynamics community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. The past decade has witnessed substantial growth in the research field of Multidisciplinary Design Optimization (MDO). MDO is a methodology for the design of complex engineering systems and subsystems that coherently exploits the synergism of mutually interacting phenomena. As evidenced by the papers, which appear in the biannual AIAA/USAF/NASA/ISSMO Symposia on Multidisciplinary Analysis and Optimization, the MDO technical community focuses on vehicle and system design issues. This paper provides an overview of the MDO technology field from a fluid dynamics perspective, giving emphasis to suggestions of specific applications of recent MDO technologies that can enhance fluid dynamics research itself across the spectrum, from basic flow physics to full configuration aerodynamics.

Zang, Thomas A.; Green, Lawrence L.

1999-01-01

336

Synthesis of Optimal Nonlinear Feedback Laws for Dynamic Systems Using Neural Networks

NASA Technical Reports Server (NTRS)

Open-loop solutions of dynamical optimization problems can be numerically computed usingexisting software packages. The computed time histories of the state and control variables, formultiple sets of end conditions can then be used to train a neural network to 'recognize' the optimal,nonlinear feedback relation between the states and controls of the system. The 'learned' network canthen be used to output an approximate optimal control given a full set (or a partial set) of measuredsystem states. With simple neural networks, we have successfully demonstrated the efficacy of theproposed approach using a minimum-time orbit injection problem. The usefulness and limitations ofthis novel approach on real-life optimal guidance and control problems, with many state and control variables as well as path inequality constraints, remain to be seen.

Lee, Allan Y.; Smyth, Padhraic

1993-01-01

337

Pinning distributed synchronization of stochastic dynamical networks: a mixed optimization approach.

This paper is concerned with the problem of pinning synchronization of nonlinear dynamical networks with multiple stochastic disturbances. Two kinds of pinning schemes are considered: 1) pinned nodes are fixed along the time evolution and 2) pinned nodes are switched from time to time according to a set of Bernoulli stochastic variables. Using Lyapunov function methods and stochastic analysis techniques, several easily verifiable criteria are derived for the problem of pinning distributed synchronization. For the case of fixed pinned nodes, a novel mixed optimization method is developed to select the pinned nodes and find feasible solutions, which is composed of a traditional convex optimization method and a constraint optimization evolutionary algorithm. For the case of switching pinning scheme, upper bounds of the convergence rate and the mean control gain are obtained theoretically. Simulation examples are provided to show the advantages of our proposed optimization method over previous ones and verify the effectiveness of the obtained results. PMID:25291734

Tang, Yang; Gao, Huijun; Lu, Jianquan; Kurths, Jürgen Kurthsrgen

2014-10-01

338

Collision-free nonuniform dynamics within continuous optimal velocity models

NASA Astrophysics Data System (ADS)

Optimal velocity (OV) car-following models give with few parameters stable stop-and -go waves propagating like in empirical data. Unfortunately, classical OV models locally oscillate with vehicles colliding and moving backward. In order to solve this problem, the models have to be completed with additional parameters. This leads to an increase of the complexity. In this paper, a new OV model with no additional parameters is defined. For any value of the inputs, the model is intrinsically asymmetric and collision-free. This is achieved by using a first-order ordinary model with two predecessors in interaction, instead of the usual inertial delayed first-order or second-order models coupled with the predecessor. The model has stable uniform solutions as well as various stable stop-and -go patterns with bimodal distribution of the speed. As observable in real data, the modal speed values in congested states are not restricted to the free flow speed and zero. They depend on the form of the OV function. Properties of linear, concave, convex, or sigmoid speed functions are explored with no limitation due to collisions.

Tordeux, Antoine; Seyfried, Armin

2014-10-01

339

Simulating the Dynamics of Scale-Free Networks via Optimization

We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution. PMID:24353752

Schieber, Tiago Alves; Ravetti, Martin Gomez

2013-01-01

340

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

341

Integration of Virtual Reality with Computational Fluid Dynamics for Process Optimization

NASA Astrophysics Data System (ADS)

Computational Fluid Dynamics (CFD) has become a powerful simulation technology used in many industrial applications for process design and optimization to save energy, improve environment, and reduce costs. In order to better understand CFD results and more easily communicate with non-CFD experts, advanced virtual reality (VR) visualization is desired for CFD post-processing. Efforts have recently been made at Purdue University Calumet to integrate VR with CFD to visualize complex data in three dimensions in an interactive, virtual environment. The virtual engineering environment greatly enhances the value of CFD simulations and allows engineers to gain much needed process insights for the design and optimization of industrial processes.

Wu, B.; Chen, G. H.; Fu, D.; Moreland, John; Zhou, Chenn Q.

2010-03-01

342

Approximate dynamic programming for optimal stationary control with control-dependent noise.

This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology. PMID:21954203

Jiang, Yu; Jiang, Zhong-Ping

2011-12-01

343

Modeling a Dynamic Design System Using the Mahalanobis Taguchi System - Two-Step Optimal Algorithm

\\u000a This work presents a novel algorithm, the Mahalanobis Taguchi System- Two Step Optimal algorithm (MTS-TSO), which combines\\u000a the Mahalanobis Taguchi System (MTS) and Two-Step Optimal (TSO) algorithm for parameter selection of product design, and parameter\\u000a adjustment under the dynamic service industry environments.\\u000a \\u000a \\u000a From the results of the confirm experiment, a service industry company is adopted to applies in the methodology,

Tsung-Shin Hsu; Ching-Lien Huang

2010-01-01

344

Analysis and formulation of a class of complex dynamic optimization problems

NASA Astrophysics Data System (ADS)

The Direct Transcription approach, also known as the direct simultaneous approach, is a widely used solution strategy for the solution of dynamic optimization problems involving differential-algebraic equations (DAEs). Direct transcription refers to the procedure of approximating the infinite dimensional problem by a finite dimensional one, which is then solved using a nonlinear programming (NLP) solver tailored to large-scale problems. Systems governed by partial differential equations (PDEs) can also be handled by spatially discretizing the PDEs to convert them to a system of DAEs. The objective of this thesis is firstly to ensure that direct transcription using Radau collocation is provably correct, and secondly to widen the applicability of the direct simultaneous approach to a larger class of dynamic optimization and optimal control problems (OCPs). This thesis aims at addressing these issues using rigorous theoretical tools and/or characteristic examples, and at the same time use the results for solving large-scale industrial applications to realize the benefits. The first part of this work deals with the analysis of convergence rates for direct transcription of unconstrained and final-time equality constrained optimal control problems. The problems are discretized using collocation at Radau points. Convergence is analyzed from an NLP/matrix-algebra perspective, which enables the prediction of the conditioning of the direct transcription NLP as the mesh size becomes finer. Several convergence results are presented along with tests on numerous example problems. These convergence results lead to an adjoint estimation procedure given the Lagrange multipliers for the large-scale NLP. The work also reveals the role of process control concepts such as controllability on the convergence analysis, and provides a very important link between control and optimization inside the framework of dynamic optimization. As an effort to extend the applicability of the direct simultaneous approach to a wider class of problems, a PDE-constrained optimal control problem, the spatial discretization of which results in a DAE-constrained problem with an arbitrarily high-index inequality constraint, is studied. Optimal control problems with high-index path constraints are very hard to solve, numerically. Contrary to the intuitive belief that the direct transcription approach would not work for the high-index optimal control problem, an analysis is performed to show that NLP-based methods have flexibility with respect to constraint qualifications, and this can be put to use in the context of high-index inequality path-constrained problems to obtain meaningful solutions. (Abstract shortened by UMI.)

Kameswaran, Shivakumar

345

A Parallel Optimization Scheme for Parameter Estimation in Motor Vehicle Dynamics

. For calibrating the vehicle model of a commercial vehicle dynamics program a parameter estimation tool has been developed which relies on observations obtained from driving tests. The associated nonlinear least-squares problem can be solved by means of mathematical optimization algorithms most of them making use of rst-order derivative information. While the complexity of the investigated vehicle dynamics program only allows the objective gradients to be approximated by means of nite dierences, this approach enables signicant savings in computational time when performing the additionally required evaluations of the objective function in parallel. The employed low-cost parallel computing platform which consists of a heterogeneous PC cluster is well suited for the needs of the automotive suppliers and industries employing vehicle dynamics simulations. 1 Introduction The numerical simulation of vehicle dynamics has gained considerable signi- cance in automotive develo...

Torsten Butz; Oskar von Stryk; Thieß-Magnus Wolter

2000-01-01

346

An enhanced integrated aerodynamic load/dynamic optimization procedure for helicopter rotor blades

NASA Technical Reports Server (NTRS)

An enhanced integrated aerodynamic load/dynamic optimization procedure is developed to minimize vibratory root shears and moments. The optimization is formulated with 4/rev vertical and 3/rev inplane shears at the blade root as objective functions and constraints, and 4/rev lagging moment. Constraints are also imposed on blade natural frequencies, weight, autorotational inertia, centrifugal stress, and rotor thrust. The 'Global Criteria Approach' is used for formulating the multi-objective optimization. Design variables include spanwise distributions of bending stiffnesses, torsional stiffness, nonstructural mass, chord, radius of gyration, and blade taper ratio. The program CAMRAD is coupled with an optimizer, which consists of the program CONMIN and an approximate analysis, to obtain optimum designs. The optimization procedure is applied to an advanced rotor as a reference design. Optimum blade designs, obtained with and without a constraint on the rotor thrust, are presented and are compared to the reference blade. Substantial reductions are obtained in the vibratory root forces and moments. As a byproduct, improvements are also found in some performance parameters, such as total power required, which were not considered during optimization.

Chattopadhyay, Aditi; Chiu, Y. Danny

1990-01-01

347

An Approach for Dynamic Optimization of Prevention Program Implementation in Stochastic Environments

NASA Astrophysics Data System (ADS)

The science of preventing youth problems has significantly advanced in developing evidence-based prevention program (EBP) by using randomized clinical trials. Effective EBP can reduce delinquency, aggression, violence, bullying and substance abuse among youth. Unfortunately the outcomes of EBP implemented in natural settings usually tend to be lower than in clinical trials, which has motivated the need to study EBP implementations. In this paper we propose to model EBP implementations in natural settings as stochastic dynamic processes. Specifically, we propose Markov Decision Process (MDP) for modeling and dynamic optimization of such EBP implementations. We illustrate these concepts using simple numerical examples and discuss potential challenges in using such approaches in practice.

Kang, Yuncheol; Prabhu, Vittal

348

PSO-based multiobjective optimization with dynamic population size and adaptive local archives.

Recently, various multiobjective particle swarm optimization (MOPSO) algorithms have been developed to efficiently and effectively solve multiobjective optimization problems. However, the existing MOPSO designs generally adopt a notion to "estimate" a fixed population size sufficiently to explore the search space without incurring excessive computational complexity. To address the issue, this paper proposes the integration of a dynamic population strategy within the multiple-swarm MOPSO. The proposed algorithm is named dynamic population multiple-swarm MOPSO. An additional feature, adaptive local archives, is designed to improve the diversity within each swarm. Performance metrics and benchmark test functions are used to examine the performance of the proposed algorithm compared with that of five selected MOPSOs and two selected multiobjective evolutionary algorithms. In addition, the computational cost of the proposed algorithm is quantified and compared with that of the selected MOPSOs. The proposed algorithm shows competitive results with improved diversity and convergence and demands less computational cost. PMID:18784011

Leong, Wen-Fung; Yen, Gary G

2008-10-01

349

A Dynamically Reconfigurable Robotic System (Concept Of A System And Optimal Configurations)

NASA Astrophysics Data System (ADS)

A new concept of robotic systems, "Dynamically Reconfigurable Robotic System(DRRS)" is shown in this paper. Each cell of the robotic module in DRRS can detach itself and combine them autonomously depending on a task, such as manipulators or mobile robots, so that the system can reorganize the optimal total shape, unlike robots developed so far which cannot reorganize automatically by changing the linkage of arms, replacing some links with others or reforming shapes in order to adapt itself to the change of working environments and demands. The newly proposed 'robotic system in this paper can be reconfigurable dynamically to a given task, so that the level of the flexibility and adaptability is much higher than that of the conventionals. DRRS has many unique adavantages, such as optimal shaping under circumstances, fault tolerance, self repairing and others. Some demonstrations can be shown experimentally and a decision method for such cell structured manipulator configurations is also proposed.

Fukuda, Toshio; Nakagawa, Seiya

1987-10-01

350

Human motion planning based on recursive dynamics and optimal control techniques

NASA Technical Reports Server (NTRS)

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.

Lo, Janzen; Huang, Gang; Metaxas, Dimitris

2002-01-01

351

Process Variation-Aware Timing Optimization for Dynamic and Mixed-Static-Dynamic CMOS Logic

The advancement in CMOS technology with the shrinking device size towards 32 nm has allowed for placement of billions of transistor on a single microprocessor chip. Simultaneously, it reduced the logic gate delays to the order of pico seconds. However, these low delays and shrinking device sizes have presented design engineers with two major challenges: timing optimization at high frequencies,

Kumar Yelamarthi; Chien-In Henry Chen

2009-01-01

352

Optimal control study for the Space Station Solar Dynamic power module

NASA Technical Reports Server (NTRS)

The authors present the design of an optimal control system for the Space Station Freedom's Solar Dynamic Fine Pointing and Tracking (SDFPT) module. A very large state model of six rigid body modes and 272 flexible modes is used in conjunction with classical LQG optimal control to produce a full-order controller which satisfies the requirements. The results obtained are compared with those of a classically designed PID (proportional plus integral plus derivative) controller that was implemented for a six-rigid-body-mode forty-flexible-mode model. A major difficulty with designing LQG controllers for large models is solving the Riccati equation that arises from the optimal formulation. A Riccati solver based on a Pade approximation to the matrix sign function is used. A symmetric version of this algorithm is derived for the special class of Hamiltonion matrices, thereby yielding, for large problems, a nearly twofold speed increase over a previous algorithm.

Papadopoulos, P. M.; Laub, A. J.; Kenney, C. S.; Pandey, P.; Ianculescu, G.; Ly, J.

1991-01-01

353

Evolutionary genetic optimization of the injector beam dynamics for the ERL test facility at IHEP

The energy recovery linac test facility (ERL-TF), a compact ERL-FEL (free electron laser) two-purpose machine, was proposed at the Institute of High Energy Physics, Beijing. As one important component of the ERL-TF, the photo-injector started with a photocathode direct-current gun was designed and preliminarily optimized. In this paper an evolutionary genetic method, non-dominated sorting genetic algorithm II, is applied to optimize the injector beam dynamics, especially in the high-charge operation mode. Study shows that using an incident laser with rms transverse size of 1~1.2 mm, the normalized emittance of the electron beam can be kept below 1 mm.mrad at the end of the injector. This work, together with the previous optimization for the low-charge operation mode by using the iterative scan method, provides guidance and confidence for future constructing and commissioning of the ERL-TF injector.

Yi, Jiao

2013-01-01

354

In a processor having multiple clusters which operate in parallel, the number of clusters in use can be varied dynamically. At the start of each program phase, the configuration option for an interval is run to determine the optimal configuration, which is used until the next phase change is detected. The optimum instruction interval is determined by starting with a minimum interval and doubling it until a low stability factor is reached.

Balasubramonian, Rajeev (Sandy, UT); Dwarkadas, Sandhya (Rochester, NY); Albonesi, David (Ithaca, NY)

2012-01-24

355

The paper is focused on the problem of determining the optimal exploitation life of the long-lasting mining machinery, such\\u000a as bucket-wheel excavators, excavators with one working element of large capacity, spreaders, self-propelled transporters,\\u000a conveyor belts and similar machinery. A concept of approach is presented, and an application of the dynamic model with the\\u000a undefined interval is given by using the

S. Vujic; I. Miljanovic; S. Maksimovic; A. Milutinovic; T. Benovic; M. Hudej; B. Dimitrijevic; V. Cebasek; G. Gajic

2010-01-01

356

NASA Astrophysics Data System (ADS)

A key challenge in managing semiarid basins, such as in the Murray-Darling in Australia, is to balance the trade-offs between the net benefits of allocating water for irrigated agriculture, and other uses, versus the costs of reduced surface flows for the environment. Typically, water planners do not have the tools to optimally and dynamically allocate water among competing uses. We address this problem by developing a general stochastic, dynamic programming model with four state variables (the drought status, the current weather, weather correlation, and current storage) and two controls (environmental release and irrigation allocation) to optimally allocate water between extractions and in situ uses. The model is calibrated to Australia's Murray River that generates: (1) a robust qualitative result that "pulse" or artificial flood events are an optimal way to deliver environmental flows over and above conveyance of base flows; (2) from 2001 to 2009 a water reallocation that would have given less to irrigated agriculture and more to environmental flows would have generated between half a billion and over 3 billion U.S. dollars in overall economic benefits; and (3) water markets increase optimal environmental releases by reducing the losses associated with reduced water diversions.

Grafton, R. Quentin; Chu, Hoang Long; Stewardson, Michael; Kompas, Tom

2011-12-01

357

NASA Astrophysics Data System (ADS)

The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc, they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions. We have also identified methods of information presentation that can considerably improve the overall performance of the system. In order to determine optimal strategies of decision guidance by means of user-specific recommendations, a stochastic behavioural description is developed. These strategies manage to increase the adaptibility to changing conditions and to reduce the deviation from the time-dependent user equilibrium, thereby enhancing the average and individual payoffs. Hence, our guidance strategies can increase the performance of all users by reducing overreaction and stabilizing the decision dynamics. These results are highly significant for predicting decision behaviour, for reaching optimal behavioural distributions by decision support systems and for information service providers. One of the promising fields of application is traffic optimization.

Helbing, Dirk; Schönhof, Martin; Kern, Daniel

2002-06-01

358

NASA Astrophysics Data System (ADS)

A comprehensive computer program is designed in MATLAB to analyze, design and optimize the propulsion, dynamics, thermodynamics, and kinematics of any serial multi-staging rocket for a set of given data. The program is quite user-friendly. It comprises two main sections: "analysis and design" and "optimization." Each section has a GUI (Graphical User Interface) in which the rocket's data are entered by the user and by which the program is run. The first section analyzes the performance of the rocket that is previously devised by the user. Numerous plots and subplots are provided to display the performance of the rocket. The second section of the program finds the "optimum trajectory" via billions of iterations and computations which are done through sophisticated algorithms using numerical methods and incremental integrations. Innovative techniques are applied to calculate the optimal parameters for the engine and designing the "optimal pitch program." This computer program is stand-alone in such a way that it calculates almost every design parameter in regards to rocket propulsion and dynamics. It is meant to be used for actual launch operations as well as educational and research purposes.

Lali, Mehdi

2009-03-01

359

Towards optimized dynamical error control and algorithms for quantum information processing

NASA Astrophysics Data System (ADS)

Two topics in the field of quantum information processing, optimized dynamical error suppression and quantum algorithms, are considered here. The computational errors induced by the surrounding environment is one of the main obstacles in building a quantum computer. Engineering powerful techniques to combat errors in quantum devices is highly demanding. In the first part of this thesis, I focus on one quantum error correction technique, dynamical decoupling (DD), introduced in Chapter 1. Chapter 2 is dedicated to nested UDD (NUDD), a highly efficient decoupling scheme that utilizes the decoupling characteristics of UDD by multi-layer nesting. UDD (1-layer NUDD) is an optimal DD method for eliminating single-qubit general dephasing, and QDD (2-layer NUDD) is a near-optimal DD method for eliminating one qubit general decoherence. I present a rigorous analytical proof of the performance and universality of QDD/NUDD, and obtain an explicit formula for the decoupling order of each error type, which elucidates the relationship between the error type and characteristics of NUDD. From the explicit formula, a NUDD scheme can be designed accordingly such that optimal efficiency of NUDD is achieved. Moreover, the highly efficient error cancellation mechanism is revealed by the analysis. The proof of QDD has been published in [31], and the proof of NUDD will be submitted for publication shortly. Chapter 3 is devoted to the Adiabatic Quantum Computation (AQC). In this work (published in [44]), a general time-optimal strategy, which in principle can optimize any quantum adiabatic algorithm for which the gap is known or can be estimated, is formulated. In addition, I present a natural differential-geometric framework for AQC. *Please refer to dissertation for diagrams.

Kuo, Wan-Jung

360

The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. PMID:22402474

Petrovi?, Jelena; Ibri?, Svetlana; Betz, Gabriele; ?uri?, Zorica

2012-05-30

361

Applicability and efficiency of near-optimal spatial encoding for dynamically adaptive MRI.

Adaptive near-optimal MRI spatial encoding entails, for the acquisition of each image update in a dynamic series, the computation of encodes in the form of a linear algebra-derived orthogonal basis set determined from an image estimate. The origins of adaptive encoding relevant to MRI are reviewed. Sources of error of this approach are identified from the linear algebraic perspective where MRI data acquisition is viewed as the projection of information from the field-of-view onto the encoding basis set. The definitions of ideal and non-ideal encoding follow, with nonideal encoding characterized by the principal angles between two vector spaces. An analysis of the distribution of principal angles is introduced and applied in several example cases to quantitatively describe the suitability of a basis set derived from a specific image estimate for the spatial encoding of a given field-of-view. The robustness of adaptive near-optimal spatial encoding for dynamic MRI is favorably shown by results computed using singular value decomposition encoding that simulates specific instances of worst case data acquisition when all objects have changed or new objects have appeared in the field-of-view. The mathematical analysis and simulations presented clarify the applicability and efficiency of adaptively determined near-optimal spatial encoding throughout a range of circumstances as may typically occur during use of dynamic MRI. PMID:9469703

Zientara, G P; Panych, L P; Jolesz, F A

1998-02-01

362

Dynamic modeling and optimal joint torque coordination of advanced robotic systems

NASA Astrophysics Data System (ADS)

The development is documented of an efficient dynamic modeling algorithm and the subsequent optimal joint input load coordination of advanced robotic systems for industrial application. A closed-form dynamic modeling algorithm for the general closed-chain robotic linkage systems is presented. The algorithm is based on the transfer of system dependence from a set of open chain Lagrangian coordinates to any desired system generalized coordinate set of the closed-chain. Three different techniques for evaluation of the kinematic closed chain constraints allow the representation of the dynamic modeling parameters in terms of system generalized coordinates and have no restriction with regard to kinematic redundancy. The total computational requirement of the closed-chain system model is largely dependent on the computation required for the dynamic model of an open kinematic chain. In order to improve computational efficiency, modification of an existing open-chain KIC based dynamic formulation is made by the introduction of the generalized augmented body concept. This algorithm allows a 44 pct. computational saving over the current optimized one (O(N4), 5995 when N = 6). As means of resolving redundancies in advanced robotic systems, local joint torque optimization is applied for effectively using actuator power while avoiding joint torque limits. The stability problem in local joint torque optimization schemes is eliminated by using fictitious dissipating forces which act in the necessary null space. The performance index representing the global torque norm is shown to be satisfactory. In addition, the resulting joint motion trajectory becomes conservative, after a transient stage, for repetitive cyclic end-effector trajectories. The effectiveness of the null space damping method is shown. The modular robot, which is built of well defined structural modules from a finite-size inventory and is controlled by one general computer system, is another class of evolving, highly versatile, advanced robotic systems. Therefore, finally, a module based dynamic modeling algorithm is presented for the dynamic coordination of such reconfigurable modular robotic systems. A user interactive module based manipulator analysis program (MBMAP) has been coded in C language running on 4D/70 Silicon Graphics.

Kang, Hee-Jun

363

A new mixed integer linear programming formulation for one ...

humans) linked by relations [4]. For example, these relations ... not work correctly in case of ties between costs, i.e. cost from one user node to two or more .... straint (2) indicates that exactly p resource nodes are established. By con- straints (3) ...

2014-05-31

364

Solving Mixed-Integer Nonlinear Programs by QP-Diving

Mar 26, 2012 ... MINLPs arise in a wide variety of applications, such as the efficient ... contingency analysis and blackout prevention of electric power ... et al., 2006; Karuppiah and Grossmann, 2006), operational reloading of nuclear reactors (Quist ... (You and Leyffer, 2010, 2011), minimum-cost designs for reinforced.

2012-03-26

365

Binary Decision Rules for Multistage Adaptive Mixed-Integer ...

Aug 20, 2014 ... decision rules and show that they are (i) highly scalable and (ii) provide high quality solutions. ... ?Sloan School of Management and Operations Research Center, ... point of view, with applications in many fields such as engineering [22, ..... is a uniform distribution supported on ? = {(?1,?2) ? R2 : ?1 = 1, ...

2014-08-20

366

Concrete Structure Design Using Mixed-Integer Nonlinear ...

Nov 24, 2009 ... entiable expressions to enforce relationships between decision variables. We represent Boolean .... The search for discrete-valued solutions using genetic algorithms is ...... nonlinear problems at every node of a tree.

2009-11-24

367

Non-Convex Mixed-Integer Nonlinear Programming: A Survey

Feb 28, 2012 ... j = 0,1,...,m are arbitrary functions mapping Z n1. + × Rn2 .... This includes, for example, water [25], gas [94], energy [100] and trans- portation ..... the space of all pairs yij as done above, this approach much more directly.

2012-02-28

368

Applications and algorithms for mixed integer nonlinear programming

insulation layer for the large hadron collider: A thermal insulation system uses a series of heat intercepts of Thermal Insulation In [6], by using MINLP, we have identified solutions that were as much as 4% better MINLP tools to address their challenging scientific problems. 2. Applications Design of thermal

Linderoth, Jeffrey T.

369

Convex Quadratic Relaxations for Mixed-Integer Nonlinear ...

providing an interesting alternative to state-of-the-art semi-definite program- ming relaxations. Three case ... 35], energy market calculations [39,37], transmission switching [16,18], dis- tribution network .... sources of power. These components ...

H. Hijazi, C. Coffrin and P. Van Hentenryck

2014-06-03

370

On the hyperplanes arrangements in mixed-integer techniques

novel element is the reduction of the number of binary variables ... path planning, ([7], [8], [9]) and air traffic management. [10]. ... quadratic) cost function has to be minimized. ..... ditional theoretical tools will be introduced in the following.

2010-09-28

371

Chance Constrained Mixed Integer Program: Bilinear and Linear ...

course decisions and their incurred cost, developed on a finite discrete scenario set. ... to build decision making tools for many real systems where uncertainty is a ...... [23] Nilay Noyan, Alternate risk measures for emergency medical service ...

2014-05-31

372

Subset Selection by Mallows' Cp: A Mixed Integer Programming ...

Graduate School of Decision Science and Technology, Tokyo Institute of .... Let us consider the case in which the number k = |S| is given, i.e., ..... [30] H. Theil, Economic Forecasts and Policy (North-Holland Publishing Company, 1961).

2014-01-15

373

A mixed integer programming approach for asset protection during ...

May 15, 2014 ... large wildfires are burning out of control and direct suppression is not a viable option. We for- mulate a .... Fire spotting, reconnaissance, air attack supervisor firefighter .... The vehicle flow to and from each location is balanced.

2014-05-15

374

Application of mixed-integer programming in chemical engineering

that had beset the research community for 15 years. The interest of a wider research community was sparked by a TV commercial in 1962 by Procter & Gamble, which promoted a competition with a $10000 prize, enough money to buy a house at the time. The task... . . . . . . . . . . . . . . 56 4.3.2.2 Convex formulations . . . . . . . . . . . . . . . . 58 vii Table of contents 4.4 Motivating study . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5...

Pogiatzis, Thomas

2013-06-11

375

A stochastic mixed integer programming approach to wildfire management systems

Wildfires have become more destructive and are seriously threatening societies and our ecosystems throughout the world. Once a wildfire escapes from its initial suppression attack, it can easily develop into a destructive huge fire that can result...

Lee, Won Ju

2009-06-02

376

Mixed Integer Second-Order Cone Programming Formulations for ...

Jun 23, 2013 ... Tokyo University of Agriculture and Technology,. 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan. bDepartment of Industrial Engineering and Management, .... to as an explained variable (or dependent variable), and xij (j = 1,2,...,k) ...... Journal of Animal Ecology, Vol.75, No.5, pp.1182–1189 (2006).

2013-06-24

377

Mixed-Integer Nonlinear Programming Michael R. Bussieck Armin Pruessner

(and even con- vex) nonlinear programs (NLP). Because subclasses MIP and NLP are among the class be a challenging and daring venture. Fortunately, the component structure of MIP and NLP within MINLP provides solutions of closely related NLP problems. For example, B&B starts out forming a pure continuous NLP problem

Neumaier, Arnold

378

Criterion Space Search Algorithms for Biobjective Mixed Integer ...

Nov 4, 2013 ... our research is a demonstration that algorithms for biobjective integer ... advantages over decision space search algorithms and are likely to be more successful. ... faster than any other criterion space search method, and (2) it ..... difference ¯H( ˜YN ) ? H( ˜YN ) provides a quantitative assessment of an ...

2013-11-04

379

Analysis of mixed integer programming formulations for single ...

An extensive computational experiment is performed to capture the strength and .... material. In the analysis of the LP relaxation, Tables 4 and 5, it is possible to ... Time-indexed formulations are known to yield better bounds, but cannot be di-.

2014-07-15

380

Mixed-Integer Models for Nonseparable Piecewise Linear ...

... circuits (Graf et al. 1990), operation planning of gas networks (Martin et al. ... one by algebraic manipulations (Tomlin 1981). However this ... linear function is motivated by the extension of this characterization to the multivariate case. A single ...

2009-06-23

381

Mixed Integer Linear Programming for Maximum-Parsimony Phylogeny Inference

of population variation data, is one of the oldest most intensively studied problems in computational biology that will be solved have been getting increasingly large in both population sizes and the numbers of variations. In this work, we focus on the inference of intraspecies phylogenies on binary genetic variation data, which

Ravi, R.

382

A Mixed Integer Nonlinear Programming Framework for Fixed Path ...

prescribed roadmaps or paths underwater. ... gramming (SQP) using multi-beam forward-looking sonar images [14], fast ... We extend the above body of work by focusing on the relatively untouched area of underwater fixed path ...... her B.S. degree in Electrical Engineering from Sharif University of Technology, Tehran, Iran,.

2013-06-10

383

Mixed Integer Programming model for pricing in telecommunication

to available resource. In a way similar to airlines, we apply YM principles to sell voice communication strategies to allocate the right capacity to the right customer at a right price and the right time in order

Paris-Sud XI, UniversitÃ© de

384

A several new mixed integer linear programming formulations for ...

May 31, 2014 ... goal is to minimize the load balance among control devices. The load balance .... proposed by Church & Cohon (1976) for siting energy facilities and afterward ..... Regional Science and Urban Economics, 17, 451–473. 12 ...

2014-05-31

385

Strong Branching Inequalities for Convex Mixed Integer Nonlinear ...

Sep 3, 2011 ... Ideally, the selection of the branching variable would lead to the smallest ... as reliability branching [3], have been implemented in state-of-the-art solvers for solving .... The valid inequalities introduced in Section 2 can be obtained almost “

2011-09-03

386

On Generalized Branching Methods for Mixed Integer Programming

Dec 31, 2004 ... are twice differentiable, and they admit a self-concordant barrier (see ...... The motivation for the particular choice of u can be seen from the following Lemma. ..... It is an exercise in algebra to show that ??xz satisfying the KKT ...

2004-12-31

387

Solving Bilevel Mixed Integer Program by Reformulations and ...

solution method to BiMIP is an effective tool that is of a great significance in practice. We organize the rest of ...... A representation and economic interpretation of a two-level ... [46] Hoang Tuy, Athanasios Migdalas, and Peter Värbrand. A global ...

2014-07-05

388

Optimization Online - A Min-Max Regret Robust Optimization ...

Aug 1, 2007 ... The structure of the first stage problem is a general mixed integer (binary) linear ... is applied to solve a number of robust facility location problems under this ... Citation: Industrial Engineering University of Houston, June 2007.

Tiravat Assavapokee

2007-08-01

389

Adaptive dynamic range optimization (ADRO) is an amplification strategy that uses digital signal processing techniques to improve the audibility, comfort, and intelligibility of sounds for people who use cochlear implants and/or hearing aids. The strategy uses statistical analysis to select the most information-rich section of the input dynamic range in multiple-frequency channels. Fuzzy logic rules control the gain in each frequency channel so that the selected section of the dynamic range is presented at an audible and comfortable level. The ADRO processing thus adaptively optimizes the dynamic range of the signal in multiple-frequency channels. Clinical studies show that ADRO can be fitted easily to all degrees of hearing loss for hearing aids and cochlear implants in a direct and intuitive manner, taking the preferences of the listener into account. The result is high acceptance by new and experienced hearing aid users and strong preferences for ADRO compared with alternative amplification strategies. The ADRO processing is particularly well suited to bimodal and hybrid stimulation which combine electric and acoustic stimulation in opposite ears or in the same ear, respectively. PMID:16012705

Blamey, Peter J.

2005-01-01

390

Optimal control theory has recently been introduced to nuclear magnetic resonance (NMR) spectroscopy as a means to systematically design and optimize pulse sequences for liquid- and solid-state applications. This has so far primarily involved numerical optimization using gradient-based methods, which allow for the optimization of a large number of pulse sequence parameters in a concerted way to maximize the efficiency of transfer between given spin states or shape the nuclear spin Hamiltonian to a particular form, both within a given period of time. Using such tools, a variety of new pulse sequences with improved performance have been developed, and the NMR spin engineers have been challenged to consider alternative routes for analytical experiment design to meet similar performance. In addition, it has lead to increasing demands to the numerical procedures used in the optimization process in terms of computational speed and fast convergence. With the latter aspect in mind, here we introduce an alternative approach to numerical experiment design based on the Krotov formulation of optimal control theory. For practical reasons, the overall radio frequency power delivered to the sample should be minimized to facilitate experimental implementation and avoid excessive sample heating. The presented algorithm makes explicit use of this requirement and iteratively solves the stationary conditions making sure that the maximum of the objective is reached. It is shown that this method is faster per iteration and takes different paths within a control space than gradient-based methods. In the present work, the Krotov approach is demonstrated by the optimization of NMR and dynamic nuclear polarization experiments for various spin systems and using different constraints with respect to radio frequency and microwave power consumption. PMID:18532824

Maximov, Ivan I; Tosner, Zden?k; Nielsen, Niels Chr

2008-05-14

391

Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks

The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach. PMID:24586257

Lung, Rodica Ioana; Chira, Camelia; Andreica, Anca

2014-01-01

392

A multi-objective dynamic programming approach to constrained discrete-time optimal control

This work presents a multi-objective differential dynamic programming approach to constrained discrete-time optimal control. In the backward sweep of the dynamic programming in the quadratic sub problem, the sub problem input at a stage or time step is solved for in terms of the sub problem state entering that stage so as to minimize the summed immediate and future cost subject to minimizing the summed immediate and future constraint violations, for all such entering states. The method differs from previous dynamic programming methods, which used penalty methods, in that the constraints of the sub problem, which may include terminal constraints and path constraints, are solved exactly if they are solvable; otherwise, their total violation is minimized. Again, the resulting solution of the sub problem is an input history that minimizes the quadratic cost function subject to being a minimizer of the total constraint violation. The expected quadratic convergence of the proposed algorithm is demonstrated on a numerical example.

Driessen, B.J.; Kwok, K.S.

1997-09-01

393

NASA Astrophysics Data System (ADS)

This article proposes a `dynamic' artificial bee colony (D-ABC) algorithm for solving optimizing problems. It overcomes the poor performance of artificial bee colony (ABC) algorithm, when applied to multi-parameters optimization. A dynamic `activity' factor is introduced to D-ABC algorithm to speed up convergence and improve the quality of solution. This D-ABC algorithm is employed for multi-parameters optimization of support vector machine (SVM)-based soft-margin classifier. Parameter optimization is significant to improve classification performance of SVM-based classifier. Classification accuracy is defined as the objection function, and the many parameters, including `kernel parameter', `cost factor', etc., form a solution vector to be optimized. Experiments demonstrate that D-ABC algorithm has better performance than traditional methods for this optimizing problem, and better parameters of SVM are obtained which lead to higher classification accuracy.

Yan, Yiming; Zhang, Ye; Gao, Fengjiao

2012-12-01

394

Computational fluid dynamics based aerodynamic optimization of the wind tunnel primary nozzle

NASA Astrophysics Data System (ADS)

The aerodynamic shape optimization of the supersonic flat nozzle is the aim of proposed paper. The nozzle discussed, is applied as a primary nozzle of the inlet part of supersonic wind tunnel. Supersonic nozzles of the measure area inlet parts need to guarantee several requirements of flow properties and quality. Mach number and minimal differences between real and required velocity and turbulence profiles at the nozzle exit are the most important parameters to meet. The aerodynamic shape optimization of the flat 2D nozzle in Computational Fluid Dynamics (CFD) is employed to reach as uniform exit velocity profile as possible, with the mean Mach number 1.4. Optimization process does not use any of standard routines of global or local optimum searching. Instead, newly formed routine, which exploits shape-based oriented sequence of nozzles, is used to research within whole discretized parametric space. The movement within optimization process is not driven by gradient or evolutionary too, instead, the Path of Minimal Shape Deformation is followed. Dynamic mesh approach is used to deform the shape and mesh from the actual nozzle to the subsequent one. Dynamic deformation of mesh allows to speed up whole converging process as an initialization of flow at the newly formed mesh is based on afore-computed shape. Shape-based similarity query in field of supersonic nozzles is discussed and applied. Evolutionary technique with genetic algorithm is used to search for minimal deformational path. As a result, the best variant from the set of solved shapes is analyzed at the base of momentum coefficient and desired Mach number at the nozzle exit.

Jan, Kolá?; Václav, Dvo?ák

2012-06-01

395

An Optimization Principle for Deriving Nonequilibrium Statistical Models of Hamiltonian Dynamics

NASA Astrophysics Data System (ADS)

A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. Given a vector of resolved variables, selected to describe the macroscopic state of the system, a family of quasi-equilibrium probability densities on phase space corresponding to the resolved variables is employed as a statistical model, and the evolution of the mean resolved vector is estimated by optimizing over paths of these densities. Specifically, a cost function is constructed to quantify the lack-of-fit to the microscopic dynamics of any feasible path of densities from the statistical model; it is an ensemble-averaged, weighted, squared-norm of the residual that results from submitting the path of densities to the Liouville equation. The path that minimizes the time integral of the cost function determines the best-fit evolution of the mean resolved vector. The closed reduced equations satisfied by the optimal path are derived by Hamilton-Jacobi theory. When expressed in terms of the macroscopic variables, these equations have the generic structure of governing equations for nonequilibrium thermodynamics. In particular, the value function for the optimization principle coincides with the dissipation potential that defines the relation between thermodynamic forces and fluxes. The adjustable closure parameters in the best-fit reduced equations depend explicitly on the arbitrary weights that enter into the lack-of-fit cost function. Two particular model reductions are outlined to illustrate the general method. In each example the set of weights in the optimization principle contracts into a single effective closure parameter.

Turkington, Bruce

2013-08-01

396

Dynamics of hepatitis C under optimal therapy and sampling based analysis

NASA Astrophysics Data System (ADS)

We examine two models for hepatitis C viral (HCV) dynamics, one for monotherapy with interferon (IFN) and the other for combination therapy with IFN and ribavirin. Optimal therapy for both the models is determined using the steepest gradient method, by defining an objective functional which minimizes infected hepatocyte levels, virion population and side-effects of the drug(s). The optimal therapies for both the models show an initial period of high efficacy, followed by a gradual decline. The period of high efficacy coincides with a significant decrease in the viral load, whereas the efficacy drops after hepatocyte levels are restored. We use the Latin hypercube sampling technique to randomly generate a large number of patient scenarios and study the dynamics of each set under the optimal therapy already determined. Results show an increase in the percentage of responders (indicated by drop in viral load below detection levels) in case of combination therapy (72%) as compared to monotherapy (57%). Statistical tests performed to study correlations between sample parameters and time required for the viral load to fall below detection level, show a strong monotonic correlation with the death rate of infected hepatocytes, identifying it to be an important factor in deciding individual drug regimens.

Pachpute, Gaurav; Chakrabarty, Siddhartha P.

2013-08-01

397

Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.

A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example. PMID:23845276

Chakraborty, Bibhas; Laber, Eric B; Zhao, Yingqi

2013-09-01

398

Disorders of the human neuromusculoskeletal system such as osteoarthritis, stroke, cerebral palsy, and paraplegia significantly affect mobility and result in a decreased quality of life. Surgical and rehabilitation treatment planning for these disorders is based primarily on static anatomic measurements and dynamic functional measurements filtered through clinical experience. While this subjective treatment planning approach works well in many cases, it does not predict accurate functional outcome in many others. This paper presents a vision for how patient-specific multibody dynamic models can serve as the foundation for an objective treatment planning approach that identifies optimal treatments and treatment parameters on an individual patient basis. First, a computational paradigm is presented for constructing patient-specific multibody dynamic models. This paradigm involves a combination of patient-specific skeletal models, muscle-tendon models, neural control models, and articular contact models, with the complexity of the complete model being dictated by the requirements of the clinical problem being addressed. Next, three clinical applications are presented to illustrate how such models could be used in the treatment design process. One application involves the design of patient-specific gait modification strategies for knee osteoarthritis rehabilitation, a second involves the selection of optimal patient-specific surgical parameters for a particular knee osteoarthritis surgery, and the third involves the design of patient-specific muscle stimulation patterns for stroke rehabilitation. The paper concludes by discussing important challenges that need to be overcome to turn this vision into reality. PMID:21785529

Fregly, Benjamin J.

2011-01-01

399

NASA Technical Reports Server (NTRS)

Empirical sizing guidelines such as tail volume coefficients have long been used in the early aircraft design phases for sizing stabilizers, resulting in conservatively stable aircraft. While successful, this results in increased empty weight, reduced performance, and greater procurement and operational cost relative to an aircraft with optimally sized surfaces. Including flight dynamics in the conceptual design process allows the design to move away from empirical methods while implementing modern control techniques. A challenge of flight dynamics and control is the numerous design variables, which are changing fluidly throughout the conceptual design process, required to evaluate the system response to some disturbance. This research focuses on addressing that challenge not by implementing higher order tools, such as computational fluid dynamics, but instead by linking the lower order tools typically used within the conceptual design process so each discipline feeds into the other. In thisresearch, flight dynamics and control was incorporated into the conceptual design process along with the traditional disciplines of vehicle sizing, weight estimation, aerodynamics, and performance. For the controller, a linear quadratic regulator structure with constant gains has been specified to reduce the user input. Coupling all the disciplines in the conceptual design phase allows the aircraft designer to explore larger design spaces where stabilizers are sized according to dynamic response constraints rather than historical static margin and volume coefficient guidelines.

Welstead, Jason; Crouse, Gilbert L., Jr.

2014-01-01

400

NASA Astrophysics Data System (ADS)

One of the biggest challenges in dynamic contrast-enhanced CT is the optimal synchronization of scan start and duration with contrast medium administration in order to optimize image contrast and to reduce the amount of contrast medium. We present a new optically based approach, which was developed to investigate and optimize bolus timing and shape. The time-concentration curve of an intravenously injected test bolus of a dye is measured in peripheral vessels with an optical sensor prior to the diagnostic CT scan. The curves can be used to assess bolus shapes as a function of injection protocols and to determine contrast medium arrival times. Preliminary results for phantom and animal experiments showed the expected linear behavior between dye concentration and absorption. The kinetics of the dye was compared to iodinated contrast medium and was found to be in good agreement. The contrast enhancement curves were reliably detected in three mice with individual bolus shapes and delay times of 2.1, 3.5 and 6.1 s, respectively. The optical sensor appears to be a promising approach to optimize injection protocols and contrast enhancement timing and is applicable to all modalities without implying any additional radiation dose. Clinical tests are still necessary.

Eisa, Fabian; Brauweiler, Robert; Peetz, Alexander; Hupfer, Martin; Nowak, Tristan; Kalender, Willi A.

2012-05-01

401

One of the biggest challenges in dynamic contrast-enhanced CT is the optimal synchronization of scan start and duration with contrast medium administration in order to optimize image contrast and to reduce the amount of contrast medium. We present a new optically based approach, which was developed to investigate and optimize bolus timing and shape. The time-concentration curve of an intravenously injected test bolus of a dye is measured in peripheral vessels with an optical sensor prior to the diagnostic CT scan. The curves can be used to assess bolus shapes as a function of injection protocols and to determine contrast medium arrival times. Preliminary results for phantom and animal experiments showed the expected linear behavior between dye concentration and absorption. The kinetics of the dye was compared to iodinated contrast medium and was found to be in good agreement. The contrast enhancement curves were reliably detected in three mice with individual bolus shapes and delay times of 2.1, 3.5 and 6.1 s, respectively. The optical sensor appears to be a promising approach to optimize injection protocols and contrast enhancement timing and is applicable to all modalities without implying any additional radiation dose. Clinical tests are still necessary. PMID:22517124

Eisa, Fabian; Brauweiler, Robert; Peetz, Alexander; Hupfer, Martin; Nowak, Tristan; Kalender, Willi A

2012-05-21

402

In this paper we report on the development of a dynamic MATLAB SIMULINK® model for the water and electrolyte balance inside the human body. This model is part of an environmentally sensitive dynamic human model for the optimization and verification of environmental control and life support systems (ECLSS) in space flight applications.An ECLSS provides all vital supplies for supporting human

P. Hager; M. Czupalla; U. Walter

2010-01-01

403

ERIC Educational Resources Information Center

There are two well-known methods for obtaining a guaranteed globally optimal solution to the problem of least-squares unidimensional scaling of a symmetric dissimilarity matrix: (a) dynamic programming, and (b) branch-and-bound. Dynamic programming is generally more efficient than branch-and-bound, but the former is limited to matrices with…

Brusco, Michael J.; Stahl, Stephanie

2005-01-01

404

This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles' activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem. PMID:24851085

Li, Desheng

2014-01-01

405

Surgeons often use spinal fixators to manage spinal instability. Dynesys (DY) is a type of dynamic fixator that is designed to restore spinal stability and to provide flexibility. The aim of this study was to design a new spinal fixator using topology optimization [the topology design (TD) system]. Here, we constructed finite element (FE) models of degenerative disc disease, DY, and the TD system. A hybrid-controlled analysis was applied to each of the three FE models. The rod structure of the topology optimization was modelled at a 39 % reduced volume compared with the rigid rod. The TD system was similar to the DY system in terms of stiffness. In contrast, the TD system reduced the cranial adjacent disc stress and facet contact force at the adjacent level. The TD system also reduced pedicle screw stresses in flexion, extension, and lateral bending. PMID:24737048

Lin, Hung-Ming; Liu, Chien-Lin; Pan, Yung-Ning; Huang, Chang-Hung; Shih, Shih-Liang; Wei, Shun-Hwa; Chen, Chen-Sheng

2014-05-01

406

Optimization of a hydrodynamic separator using a multiscale computational fluid dynamics approach.

This article deals with the optimization of a hydrodynamic separator working on the tangential separation mechanism along a screen. The aim of this study is to optimize the shape of the device to avoid clogging. A multiscale approach is used. This methodology combines measurements and computational fluid dynamics (CFD). A local model enables us to observe the different phenomena occurring at the orifice scale, which shows the potential of expanded metal screens. A global model is used to simulate the flow within the device using a conceptual model of the screen (porous wall). After validation against the experimental measurements, the global model was used to investigate the influence of deflectors and disk plates in the structure. PMID:24135107

Schmitt, Vivien; Dufresne, Matthieu; Vazquez, Jose; Fischer, Martin; Morin, Antoine

2013-01-01

407

Dynamic optimization model of energy related economic planning and development for the Navajo nation

The Navajo reservation located in portions of Arizona, New Mexico and Utah is rich in low sulfur coal deposits, ideal for strip mining operation. The Navajo Nation has been leasing the mineral resources to non-Indian enterprises for purposes of extraction. Since the early 1950s the Navajo Nation has entered into extensive coal leases with several large companies and utilities. Contracts have committed huge quantities of Navajo coal for mining. This research was directed to evaluate the shadow prices of Navajo coal and identify optimal coal extraction. An economic model of coal resource extraction over time was structured within an optimal control theory framework. The control problem was formulated as a discrete dynamic optimization problem. A comparison of the shadow prices of coal deposits derived from the dynamic model with the royalty payments the tribe receives on the basis of the present long-term lease contracts indicates that, in most cases, the tribe is paid considerably less than the amount of royalty projected by the model. Part of these discrepancies may be explained in terms of the low coal demand condition at the time of leasing and due to greater uncertainties with respect to the geologic information and other risks associated with mining operations. However, changes in the demand for coal with rigidly fixed terms of royalty rates will lead to non-optimal extraction of coal. A corrective tax scheme is suggested on the basis of the results of this research. The proposed tax per unit of coal shipped from a site is the difference between the shadow price and the present royalty rate. The estimated tax rates over time are derived.

Beladi, S.A.

1983-01-01

408

Solving the equation for the Iberian upwelling biogeochemical dynamics: an optimization experience

NASA Astrophysics Data System (ADS)

Trying to find a set of parameters to properly reproduce the biogeochemical dynamics of the region of study is a major concern in biogeochemical ocean modelling. Model parameters are constant values introduced in the equations that calculate the time and space evolution of the state variables of the biogeochemical model. A good set of parameters allows for a better representation of the biological and chemical processes in the system, and thus to model results more approximated to reality. However, it is not a straightforward task, because many parameters are not well constrained in the literature, or they may be unknown or vary considerably between different regions. Usually, the approach to find the appropriate values is running several simulations, after some sensitivity test to individual parameters, until a satisfactory result is obtained. This may be very time consuming and quite subjective. A more systematic way to find this set of parameters has arisen over the last years by using mathematical optimization techniques. The basic principle under optimization is to minimize the difference between an observed and a simulated data series by using a cost function. We have applied an optimization technique to find an appropriate set of parameters for modelling the biogeochemical dynamics of the western Iberian shelf, off the Atlantic coast of Portugal and Galicia (NW Spain), which is characterized by a conspicuous seasonal upwelling. The ocean model is a high resolution 3D regional configuration of ROMS coupled to a N2PZD2 biogeochemical model. Results using the a priori parameters and the optimized parameters are compared and discussed. The study is the result of a multidisciplinary collaborative effort between the University of Aveiro ocean modelling group (Portugal), the ETHZ (Switzerland) and the IIM-CSIC Vigo oceanography group (Spain).

Reboreda, R.; Santaren, D.; Castro, C. G.; Alvarez-Salgado, X. A.; Nolasco, R.; Queiroga, H.; Dubert, J.

2012-04-01

409

The importance of functional form in optimal control solutions of problems in population dynamics

Optimal control theory is finding increased application in both theoretical and applied ecology, and it is a central element of adaptive resource management. One of the steps in an adaptive management process is to develop alternative models of system dynamics, models that are all reasonable in light of available data, but that differ substantially in their implications for optimal control of the resource. We explored how the form of the recruitment and survival functions in a general population model for ducks affected the patterns in the optimal harvest strategy, using a combination of analytical, numerical, and simulation techniques. We compared three relationships between recruitment and population density (linear, exponential, and hyperbolic) and three relationships between survival during the nonharvest season and population density (constant, logistic, and one related to the compensatory harvest mortality hypothesis). We found that the form of the component functions had a dramatic influence on the optimal harvest strategy and the ultimate equilibrium state of the system. For instance, while it is commonly assumed that a compensatory hypothesis leads to higher optimal harvest rates than an additive hypothesis, we found this to depend on the form of the recruitment function, in part because of differences in the optimal steady-state population density. This work has strong direct consequences for those developing alternative models to describe harvested systems, but it is relevant to a larger class of problems applying optimal control at the population level. Often, different functional forms will not be statistically distinguishable in the range of the data. Nevertheless, differences between the functions outside the range of the data can have an important impact on the optimal harvest strategy. Thus, development of alternative models by identifying a single functional form, then choosing different parameter combinations from extremes on the likelihood profile may end up producing alternatives that do not differ as importantly as if different functional forms had been used. We recommend that biological knowledge be used to bracket a range of possible functional forms, and robustness of conclusions be checked over this range.

Runge, M.C.; Johnson, F.A.

2002-01-01

410

High-Dynamic-Range Imaging of Nanoscale Magnetic Fields Using Optimal Control of a Single Qubit

NASA Astrophysics Data System (ADS)

We present a novel spectroscopy protocol based on optimal control of a single quantum system. It enables measurements with quantum-limited sensitivity (???(1/T2*), T2* denoting the system’s coherence time) but has an orders of magnitude larger dynamic range than pulsed spectroscopy methods previously employed for this task. We employ this protocol to image nanoscale magnetic fields with a single scanning nitrogen-vacancy center in diamond. Here, our scheme enables quantitative imaging of a strongly inhomogeneous field in a single scan without closed-loop control, which has previously been necessary to achieve this goal.

Häberle, T.; Schmid-Lorch, D.; Karrai, K.; Reinhard, F.; Wrachtrup, J.

2013-10-01

411

A framework for optimal temporal reduced order modeling of nonlinear dynamical systems

NASA Astrophysics Data System (ADS)

An optimal temporal reduced order modeling framework is proposed for nonlinear dynamical systems. The governing equations are modified for an under-resolved simulation with an arbitrary scheme and a coarse temporal grid. Subgrid-scale models are developed to account for the unresolved temporal structure via inclusion of statistical information on a multi-point temporal stencil. These models are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation. In order to validate the proposed framework, we investigate time-periodic solutions for a canonical Duffing oscillator using a coarse harmonic balance scheme. In order to demonstrate application of the proposed framework to a high dimensional nonlinear dynamical system, we also investigate a simply supported, geometrically nonlinear beam under the influence of time-periodic external forcing. For both problems, the subgrid-scale models are shown to significantly improve the accuracy of coarse-grained solutions.

LaBryer, A.; Attar, P. J.; Vedula, P.

2013-02-01

412

Dynamic optimization in the design and scheduling of multiproduct batch plants

Dynamic batch processing provides additional transient operating freedom, that can stretch the limits of profitability under strict market, facility, and time constraints. The paper incorporates dynamic processing conditions for products in a multiproduct batch plant, as opposed to fixing the process by recipes, in the broader context of equipment design, production planning, scheduling, and inventory considerations. The objective is a general function of fixed design costs, operating costs, production revenues, etc. Decisions include stage processing times for products, transient stage operating policies, continuous design parameters, production capacity, and production schedules. The infinite dimensional optimal control problem for each operation is solved using collocation over finite time elements. Scheduling, with its combinatorial complexity, is addressed in the scope of flowshop plants for specific transfer policies using the aggregated scheduling model in the cited references.

Bhatia, T.; Biegler, L.T. [Carnegie-Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering] [Carnegie-Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering

1996-07-01

413

NASA Astrophysics Data System (ADS)

The Dynamic economic dispatch (DED) problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. Recently social foraging behavior of Escherichia coli bacteria has been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFO algorithms with varying chemo tactic step size for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with those methods reported in the literature. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units.

Praveena, P.; Vaisakh, K.; Rama Mohana Rao, S.

414

Dynamic optimization for commercialization of renewable energy: an example for solar photovoltaics

There are several studies of optimal allocation of research and development resources over the time horizon of a project. The primary result of the basic noncompetitive models in this literature is that the optimal strategy is to choose a research intensity and ending date for the project such that the marginal costs of accelerating the project equals the marginal benefits of introducing the product sooner. This literature provides useful insights for the government planner who must allocate R&D resources for renewable energy development. However, several characteristics distinguish the process from the typical R&D planning problem. Specifically, with PV development, where the goal is to maximize the net present value of activities leading to cost reduction in commercial modules, there are (1) significant lag-times between investment in laboratory research and resulting effects in the marketplace, (2) a learning curve associated with the manufacturing process that also reduces the cost s of PV modules, (3) interim benefits from technical advances, (4) no clear end point to the R&D process, but rather a tapering off of the value of advances in technical efficiency, (5) significant uncertainty in the R&D process, (6) a family of products rather than an individual technology, (7) a co-mingling of government and private resources with implications for efficient management. A dynamic model is developed to characterize the optimal intensity and timing of government and private resource allocation for basic research in improving the technical efficiency of cells and subsidies to the manufacturing process to encourage progress on the learning curve. A series of propositions regarding optimal paths for each are examined. While the research is purely analytical, the results are useful for conceptualizing the R&D planning process. They also provide a basis for a numerical study that can address whether current levels and historic patterns of funding are optimal.

Richards, Kenneth, R.; Ashton, W. Bradley; McVeigh, James

2000-04-21

415

NASA Astrophysics Data System (ADS)

This paper compares particle swarm optimization (PSO) techniques for a reactive power allocation planning problem in power systems. The problem can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The PSO based methods determines a reactive power allocation strategy with continuous and discrete state variables such as automatic voltage regulator (AVR) operating values of electric power generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. Namely, this paper investigates applicability of PSO techniques to one of the practical MINLPs in power systems. Four variations of PSO: PSO with inertia weight approach (IWA), PSO with constriction factor approach (CFA), hybrid particle swarm optimization (HPSO) with IWA, and HPSO with CFA are compared. The four methods are applied to the standard IEEE14 bus system and a practical 112 bus system.

Fukuyama, Yoshikazu

416

Optimal Reaction Coordinate as a Biomarker for the Dynamics of Recovery from Kidney Transplant

The evolution of disease or the progress of recovery of a patient is a complex process, which depends on many factors. A quantitative description of this process in real-time by a single, clinically measurable parameter (biomarker) would be helpful for early, informed and targeted treatment. Organ transplantation is an eminent case in which the evolution of the post-operative clinical condition is highly dependent on the individual case. The quality of management and monitoring of patients after kidney transplant often determines the long-term outcome of the graft. Using NMR spectra of blood samples, taken at different time points from just before to a week after surgery, we have shown that a biomarker can be found that quantitatively monitors the evolution of a clinical condition. We demonstrate that this is possible if the dynamics of the process is considered explicitly: the biomarker is defined and determined as an optimal reaction coordinate that provides a quantitatively accurate description of the stochastic recovery dynamics. The method, originally developed for the analysis of protein folding dynamics, is rigorous, robust and general, i.e., it can be applied in principle to analyze any type of biological dynamics. Such predictive biomarkers will promote improvement of long-term graft survival after renal transplantation, and have potentially unlimited applications as diagnostic tools. PMID:24967678

Krivov, Sergei V.; Fenton, Hayley; Goldsmith, Paul J.; Prasad, Rajendra K.; Fisher, Julie; Paci, Emanuele

2014-01-01

417

Network dynamics for optimal compressive-sensing input-signal recovery

NASA Astrophysics Data System (ADS)

By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs.

Barranca, Victor J.; Kova?i?, Gregor; Zhou, Douglas; Cai, David

2014-10-01

418

An adaptive differential evolution algorithm for global optimization in dynamic environments.

This article proposes a multipopulation-based adaptive differential evolution (DE) algorithm to solve dynamic optimization problems (DOPs) in an efficient way. The algorithm uses Brownian and adaptive quantum individuals in conjunction with the DE individuals to maintain the diversity and exploration ability of the population. This algorithm, denoted as dynamic DE with Brownian and quantum individuals (DDEBQ), uses a neighborhood-driven double mutation strategy to control the perturbation and thereby prevents the algorithm from converging too quickly. In addition, an exclusion rule is used to spread the subpopulations over a larger portion of the search space as this enhances the optima tracking ability of the algorithm. Furthermore, an aging mechanism is incorporated to prevent the algorithm from stagnating at any local optimum. The performance of DDEBQ is compared with several state-of-the-art evolutionary algorithms using a suite of benchmarks from the generalized dynamic benchmark generator (GDBG) system used in the competition on evolutionary computation in dynamic and uncertain environments, held under the 2009 IEEE Congress on Evolutionary Computation (CEC). The simulation results indicate that DDEBQ outperforms other algorithms for most of the tested DOP instances in a statistically meaningful way. PMID:23996590

Das, Swagatam; Mandal, Ankush; Mukherjee, Rohan

2014-06-01

419

In the present paper, the endogenous theory of time preference is extended to analyze those processes of capital accumulation and changes in environmental quality that are dynamically optimum with respect to the intertemporal preference ordering of the representative individual of the society in question. The analysis is carried out within the conceptual framework of the dynamic analysis of environmental quality, as has been developed by a number of economists for specific cases of the fisheries and forestry commons. The duality principles on intertemporal preference ordering and capital accumulation are extended to the situation where processes of capital accumulation are subject to the Penrose effect, which exhibit the marginal decrease in the effect of investment in private and social overhead capital upon the rate at which capital is accumulated. The dynamically optimum time-path of economic activities is characterized by the proportionality of two systems of imputed, or efficient, prices, one associated with the given intertemporal ordering and another associated with processes of accumulation of private and social overhead capital. It is particularly shown that the dynamically optimality of the processes of capital accumulation involving both private and social overhead capital is characterized by the conditions that are identical with those involving private capital, with the role of social overhead capital only indirectly exhibited. PMID:11607685

Uzawa, H

1996-01-01

420

Optimal dynamic voltage scaling for wireless sensor nodes with real-time constraints

NASA Astrophysics Data System (ADS)

Sensors are increasingly embedded in manufacturing systems and wirelessly networked to monitor and manage operations ranging from process and inventory control to tracking equipment and even post-manufacturing product monitoring. In building such sensor networks, a critical issue is the limited and hard to replenish energy in the devices involved. Dynamic voltage scaling is a technique that controls the operating voltage of a processor to provide desired performance while conserving energy and prolonging the overall network's lifetime. We consider such power-limited devices processing time-critical tasks which are non-preemptive, aperiodic and have uncertain arrival times. We treat voltage scaling as a dynamic optimization problem whose objective is to minimize energy consumption subject to hard or soft real-time execution constraints. In the case of hard constraints, we build on prior work (which engages a voltage scaling controller at task completion times) by developing an intra-task controller that acts at all arrival times of incoming tasks. We show that this optimization problem can be decomposed into two simpler ones whose solution leads to an algorithm that does not actually require solving any nonlinear programming problems. In the case of soft constraints, this decomposition must be partly relaxed, but it still leads to a scalable (linear in the number of tasks) algorithm. Simulation results are provided to illustrate performance improvements in systems with intra-task controllers compared to uncontrolled systems or those using inter-task control.

Cassandras, Christos G.; Zhuang, Shixin

2005-11-01

421

Dynamical-decoupling noise spectroscopy at an optimal working point of a qubit

NASA Astrophysics Data System (ADS)

I present a theory of environmental noise spectroscopy via dynamical decoupling of a qubit at an optimal working point. Considering a sequence of n pulses and pure dephasing due to quadratic coupling to Gaussian distributed noise ? (t), I use the linked-cluster (cumulant) expansion to calculate the coherence decay. Solutions allowing for reconstruction of spectral density of noise are given. For noise with correlation time shorter than the time scale on which coherence decays, the noise filtered by the dynamical decoupling procedure can be treated as effectively Gaussian at large n, and well-established methods of noise spectroscopy can be used to reconstruct the spectrum of ?2(t) noise. On the other hand, for noise of dominant low-frequency character (1/f? noise with ? >1), an infinite-order resummation of the cumulant expansion is necessary, and it leads to an analytical formula for coherence decay having a power-law tail at long times. In this case, the coherence at time t depends both on spectral density of ? (t) noise at ? =n?/t, and on the effective low-frequency cutoff of the noise spectrum, which is typically given by the inverse of the data acquisition time. Simulations of decoherence due to purely transverse noise show that the analytical formulas derived in this paper apply in this often encountered case of an optimal working point, provided that the number of pulses is not very large, and that the longitudinal qubit splitting is much larger than the transverse noise amplitude.

Cywi?ski, ?ukasz

2014-10-01

422

Fluid-Dynamic Optimal Design of Helical Vascular Graft for Stenotic Disturbed Flow

Although a helical configuration of a prosthetic vascular graft appears to be clinically beneficial in suppressing thrombosis and intimal hyperplasia, an optimization of a helical design has yet to be achieved because of the lack of a detailed understanding on hemodynamic features in helical grafts and their fluid dynamic influences. In the present study, the swirling flow in a helical graft was hypothesized to have beneficial influences on a disturbed flow structure such as stenotic flow. The characteristics of swirling flows generated by helical tubes with various helical pitches and curvatures were investigated to prove the hypothesis. The fluid dynamic influences of these helical tubes on stenotic flow were quantitatively analysed by using a particle image velocimetry technique. Results showed that the swirling intensity and helicity of the swirling flow have a linear relation with a modified Germano number (Gn*) of the helical pipe. In addition, the swirling flow generated a beneficial flow structure at the stenosis by reducing the size of the recirculation flow under steady and pulsatile flow conditions. Therefore, the beneficial effects of a helical graft on the flow field can be estimated by using the magnitude of Gn*. Finally, an optimized helical design with a maximum Gn* was suggested for the future design of a vascular graft. PMID:25360705

Ha, Hojin; Hwang, Dongha; Choi, Woo-Rak; Baek, Jehyun; Lee, Sang Joon

2014-01-01

423

NASA Astrophysics Data System (ADS)

Fundamental molecular selectivity limits are probed by exploiting laser-controlled quantum interferences for the creation of distinct spectral signatures in two flavin molecules, erstwhile nearly indistinguishable via steady-state methods. Optimal dynamic discrimination (ODD) uses optimally shaped laser fields to transiently amplify minute molecular variations that would otherwise go unnoticed with linear absorption and fluorescence techniques. ODD is experimentally demonstrated by combining an optimally shaped UV pump pulse with a time-delayed, fluorescence-depleting IR pulse for discrimination amongst riboflavin and flavin mononucleotide in aqueous solution, which are structurally and spectroscopically very similar. Closed-loop, adaptive pulse shaping discovers a set of UV pulses that induce disparate responses from the two flavins and allows for concomitant flavin discrimination of ˜16?. Additionally, attainment of ODD permits quantitative, analytical detection of the individual constituents in a flavin mixture. The successful implementation of ODD on quantum systems of such high complexity bodes well for the future development of the field and the use of ODD techniques in a variety of demanding practical applications.

Roslund, Jonathan; Roth, Matthias; Guyon, Laurent; Boutou, Véronique; Courvoisier, Francois; Wolf, Jean-Pierre; Rabitz, Herschel

2011-01-01

424

NASA Astrophysics Data System (ADS)

This paper proposes a novel sub-architecture to optimize the data flow of REMUS-II (REconfigurable MUltimedia System 2), a dynamically coarse grain reconfigurable architecture. REMUS-II consists of a µPU (Micro-Processor Unit) and two RPUs (Reconfigurable Processor Unit), which are used to speeds up control-intensive tasks and data-intensive tasks respectively. The parallel computing capability and flexibility of REMUS-II makes itself an excellent candidate to process multimedia applications, which require a large amount of memory accesses. In this paper, we specifically optimize the data flow to deal with those performance-hazard and energy-hungry memory accessing in order to meet the bandwidth requirement of parallel computing. The RPU internal memory could work in multiple modes, like 2D-access mode and transformation mode, according to different multimedia access patterns. This novel design can improve the performance up to 26% compared to traditional on-chip memory. Meanwhile, the block buffer is implemented to optimize the off-chip data flow through reducing off-chip memory accesses, which reducing up to 43% compared to direct DDR access. Based on RTL simulation, REMUS-II can achieve 1080p@30fps of H.264 High Profile@ Level 4 and High Level MPEG2 at 200MHz clock frequency. The REMUS-II is implemented into 23.7mm2 silicon on TSMC 65nm logic process with a 400MHz maximum working frequency.

Liu, Xinning; Mei, Chen; Cao, Peng; Zhu, Min; Shi, Longxing

425

Time-optimal path planning in dynamic flows using level set equations: theory and schemes

NASA Astrophysics Data System (ADS)

We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.

Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.

2014-09-01

426

NASA Technical Reports Server (NTRS)

This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.

Nguyen, Nhan

2013-01-01

427

Time-optimal path planning in dynamic flows using level set equations: theory and schemes

NASA Astrophysics Data System (ADS)

We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.

Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.

2014-10-01

428

Optimal Identification of Semi-Rigid Domains in Macromolecules from Molecular Dynamics Simulation

Biological function relies on the fact that biomolecules can switch between different conformations and aggregation states. Such transitions involve a rearrangement of parts of the biomolecules involved that act as dynamic domains. The reliable identification of such domains is thus a key problem in biophysics. In this work we present a method to identify semi-rigid domains based on dynamical data that can be obtained from molecular dynamics simulations or experiments. To this end the average inter-atomic distance-deviations are computed. The resulting matrix is then clustered by a constrained quadratic optimization problem. The reliability and performance of the method are demonstrated for two artificial peptides. Furthermore we correlate the mechanical properties with biological malfunction in three variants of amyloidogenic transthyretin protein, where the method reveals that a pathological mutation destabilizes the natural dimer structure of the protein. Finally the method is used to identify functional domains of the GroEL-GroES chaperone, thus illustrating the efficiency of the method for large biomolecular machines. PMID:20498702

Bernhard, Stefan; Noe, Frank

2010-01-01

429

Conclusions \\u000a \\u000a \\u000a \\u000a 1. \\u000a \\u000a The problem of optimizing the initial filling regime of large multiannual regulation reservoirs is a part of the general problem\\u000a of optimizing the operation of a hydroelectric plant in a power system.\\u000a \\u000a \\u000a \\u000a \\u000a 2. \\u000a \\u000a The optimization of the initial reservoir filling regime is possible by using one of the dynamic programming algorithms, which\\u000a enables solving the problem for minimum

D. N. Korobova

1968-01-01

430

This thesis develops a new optimal control theory for a class of distributed-parameter systems governed by first-order quasilinear hyperbolic partial differential equations that arise in many physical applications such as fluid dynamics problems. These systems are controlled at their boundaries via boundary controls that are subject to dynamic constraints imposed by lumped-parameter systems governed by ordinary differential equations. A Mach

Nhan T. Nguyen

2005-01-01

431

An ``optimal'' spawning algorithm for adaptive basis set expansion in nonadiabatic dynamics

NASA Astrophysics Data System (ADS)

The full multiple spawning (FMS) method has been developed to simulate quantum dynamics in the multistate electronic problem. In FMS, the nuclear wave function is represented in a basis of coupled, frozen Gaussians, and a ``spawning'' procedure prescribes a means of adaptively increasing the size of this basis in order to capture population transfer between electronic states. Herein we detail a new algorithm for specifying the initial conditions of newly spawned basis functions that minimizes the number of spawned basis functions needed for convergence. ``Optimally'' spawned basis functions are placed to maximize the coupling between parent and child trajectories at the point of spawning. The method is tested with a two-state, one-mode avoided crossing model and a two-state, two-mode conical intersection model.

Yang, Sandy; Coe, Joshua D.; Kaduk, Benjamin; Martínez, Todd J.

2009-04-01

432

Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes

In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a key decision point and dictates the next treatment action among the options available as a function of accrued information on the patient. Using data from a clinical trial or observational study, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q-learning and advantage (A-)learning are two main approaches for this purpose. We provide a detailed account of Q- and A-learning and study systematically the performance of these methods. The methods are illustrated using data from a study of depression.

Schulte, Phillip J; Laber, Eric B; Davidian, Marie

2012-01-01

433

An 'optimal' spawning algorithm for adaptive basis set expansion in nonadiabatic dynamics

The full multiple spawning (FMS) method has been developed to simulate quantum dynamics in the multistate electronic problem. In FMS, the nuclear wave function is represented in a basis of coupled, frozen Gaussians, and a 'spawning' procedure prescribes a means of adaptively increasing the size of this basis in order to capture population transfer between electronic states. Herein we detail a new algorithm for specifying the initial conditions of newly spawned basis functions that minimizes the number of spawned basis functions needed for convergence. 'Optimally' spawned basis functions are placed to maximize the coupling between parent and child trajectories at the point of spawning. The method is tested with a two-state, one-mode avoided crossing model and a two-state, two-mode conical intersection model.

Yang, Sandy; Coe, Joshua D.; Kaduk, Benjamin; Martinez, Todd J. [Department of Chemistry and Beckman Institute, University of Illinois at Urbana-Champaign, 600 S. Mathews Ave., Urbana, Illinois 61801 (United States)

2009-04-07

434

Sequentially Optimized Meshfree Approximation as a New Computational Fluid Dynamics Solver

NASA Astrophysics Data System (ADS)

This thesis presents the Sequentially Optimized Meshfree Approximation (SOMA) method, a new and powerful Computational Fluid Dynamics (CFD) solver. While standard computational methods can be faster and cheaper that physical experimentation, both in cost and work time, these methods do have some time and user interaction overhead which SOMA eliminates. As a meshfree method which could use adaptive domain refinement methods, SOMA avoids the need for user generated and/or analyzed grids, volumes, and meshes. Incremental building of a feed-forward artificial neural network through machine learning to solve the flow problem significantly reduces user interaction and reduces computational cost. This is done by avoiding the creation and inversion of possibly dense block diagonal matrices and by focusing computational work on regions where the flow changes and ignoring regions where no changes occur.

Wilkinson, Matthew

435

Design and construction of miniature artificial ecosystem based on dynamic response optimization

NASA Astrophysics Data System (ADS)

The miniature artificial ecosystem (MAES) is a combination of man, silkworm, salad and mi-croalgae to partially regenerate O2 , sanitary water and food, simultaneously dispose CO2 and wastes, therefore it have a fundamental life support function. In order to enhance the safety and reliability of MAES and eliminate the influences of internal variations and external dis-turbances, it was necessary to configure MAES as a closed-loop control system, and it could be considered as a prototype for future bioregenerative life support system. However, MAES is a complex system possessing large numbers of parameters, intricate nonlinearities, time-varying factors as well as uncertainties, hence it is difficult to perfectly design and construct a prototype through merely conducting experiments by trial and error method. Our research presented an effective way to resolve preceding problem by use of dynamic response optimiza-tion. Firstly the mathematical model of MAES with first-order nonlinear ordinary differential equations including parameters was developed based on relevant mechanisms and experimental data, secondly simulation model of MAES was derived on the platform of MatLab/Simulink to perform model validation and further digital simulations, thirdly reference trajectories of de-sired dynamic response of system outputs were specified according to prescribed requirements, and finally optimization for initial values, tuned parameter and independent parameters was carried out using the genetic algorithm, the advanced direct search method along with parallel computing methods through computer simulations. The result showed that all parameters and configurations of MAES were determined after a series of computer experiments, and its tran-sient response performances and steady characteristics closely matched the reference curves. Since the prototype is a physical system that represents the mathematical model with reason-able accuracy, so the process of designing and constructing a prototype of MAES is the reverse of mathematical modeling, and must have prerequisite assists from these results of computer simulation.

Hu, Dawei; Liu, Hong; Tong, Ling; Li, Ming; Hu, Enzhu

436

NASA Astrophysics Data System (ADS)

In meteorological and oceanological studies the classical approach for finding the numerical solution of the regional model consists in formulating and solving a Cauchy-Dirichlet problem. The boundary conditions are obtained by linear interpolation of coarse-grid data provided by a global model. Errors in boundary conditions due to interpolation may cause large deviations from the correct regional solution. The methods developed to reduce these errors deal with continuous dynamic assimilation of known global data available inside the regional domain. One of the approaches of this assimilation procedure performs a nudging of large-scale components of regional model solution to large-scale global data components by introducing relaxation forcing terms into the regional model equations. As a result, the obtained solution is not a valid numerical solution to the original regional model. Another approach is the use a four-dimensional variational data assimilation procedure which is free from the above-mentioned shortcoming. In this work we formulate the joint problem of finding the regional model solution and data assimilation as a PDE-constrained optimization problem. Three simple model examples (ODE Burgers equation, Rossby-Oboukhov equation, Korteweg-de Vries equation) are considered in this paper. Numerical experiments indicate that the optimization approach can significantly improve the precision of the regional solution.

Pisnitchenko, F. I.; Pisnichenko, I. A.; Martínez, J. M.; Santos, S. A.

2008-11-01

437

Dynamically optimizing experiment schedules of a laboratory robot system with simulated annealing.

A scheduler has been developed for an integrated laboratory robot system that operates in an always-on mode. The integrated system is designed for imaging plates containing protein crystallization experiments, and it allows crystallographers to enter plates at any time and request that they be imaged at multiple time points in the future. The scheduler must rearrange tasks within the time it takes to image one plate, trading off the quality of the schedule for the speed of the computation. For this reason, the scheduler was based on a simulated annealing algorithm with an objective function that makes use of a linear programming solver. To optimize the scheduler, extensive computational simulations were performed involving a difficult but representative scheduling problem. The simulations explore multiple configurations of the simulated annealing algorithm, including both geometric and adaptive annealing schedules, 3 neighborhood functions, and 20 neighborhood diameters. An optimal configuration was found that produced the best results in less than 60 seconds, well within the window necessary to dynamically reschedule imaging tasks as new plates are entered into the system. PMID:25117530

Cabrera, Cristina; Fine-Morris, Morgan; Pokross, Matthew; Kish, Kevin; Michalczyk, Stephen; Cahn, Matthew; Klei, Herbert; Russo, Mark F

2014-12-01

438

Multidisciplinary optimization for the design and control of uncertain dynamical systems

NASA Astrophysics Data System (ADS)

This dissertation considers an integrated approach to system design and controller design based on analyzing limits of system performance. Historically, plant design methodologies have not incorporated control relevant considerations. Such an approach could result in a system that might not meet its specifications (or one that requires a complex control architecture to do so). System and controller designers often go through several iterations in order to converge to an acceptable plant and controller design. The focus of this dissertation is on the design and control an air-breathing hypersonic vehicle using such an integrated system-control design framework. The goal is to reduce the number of system-control design iterations (by explicitly incorporate control considerations in the system design process), as well as to influence the guidance/trajectory specifications for the system. Due to the high computational costs associated with obtaining a dynamic model for each plant configuration considered, approximations to the system dynamics are used in the control design process. By formulating the control design problem using bilinear and polynomial matrix inequalities, several common control and system design constraints can be simultaneously incorporated into a vehicle design optimization. Several design problems are examined to illustrate the effectiveness of this approach (and to compare the computational burden of this methodology against more traditional approaches).

Sridharan, Srikanth

439

A parallel dynamic programming algorithm for multi-reservoir system optimization

NASA Astrophysics Data System (ADS)

This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system. First, a multi-dimensional dynamic programming (DP) model is formulated for a multi-reservoir system. Second, the DP algorithm is parallelized using a peer-to-peer parallel paradigm. The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol. We consider both the distributed computing and distributed computer memory in the parallelization. The parallel paradigm aims at reducing the computation time as well as alleviating the computer memory requirement associated with running a multi-dimensional DP model. Next, we test the parallel DP algorithm on the classic, benchmark four-reservoir problem on a high-performance computing (HPC) system with up to 350 cores. Results indicate that the parallel DP algorithm exhibits good performance in parallel efficiency; the parallel DP algorithm is scalable and will not be restricted by the number of cores. Finally, the parallel DP algorithm is applied to a real-world, five-reservoir system in China. The results demonstrate the parallel efficiency and practical utility of the proposed methodology.

Li, Xiang; Wei, Jiahua; Li, Tiejian; Wang, Guangqian; Yeh, William W.-G.

2014-05-01

440

DYNAMIC RIDE-SHARING AND OPTIMAL FLEET SIZING FOR A SYSTEM OF1 SHARED AUTONOMOUS VEHICLES2

DYNAMIC RIDE-SHARING AND OPTIMAL FLEET SIZING FOR A SYSTEM OF1 SHARED AUTONOMOUS VEHICLES2 3 4 and for publication in Transportation21 22 23 ABSTRACT24 25 Shared autonomous (fully-automated) vehicles (SAVs,16 to anticipate SAV system implications for various shares of travelers who had previously17

Kockelman, Kara M.

441

Decomposition and coordination of large-scale operations optimization

NASA Astrophysics Data System (ADS)

Nowadays, highly integrated manufacturing has resulted in more and more large-scale industrial operations. As one of the most effective strategies to ensure high-level operations in modern industry, large-scale engineering optimization has garnered a great amount of interest from academic scholars and industrial practitioners. Large-scale optimization problems frequently occur in industrial applications, and many of them naturally present special structure or can be transformed to taking special structure. Some decomposition and coordination methods have the potential to solve these problems at a reasonable speed. This thesis focuses on three classes of large-scale optimization problems: linear programming, quadratic programming, and mixed-integer programming problems. The main contributions include the design of structural complexity analysis for investigating scaling behavior and computational efficiency of decomposition strategies, novel coordination techniques and algorithms to improve the convergence behavior of decomposition and coordination methods, as well as the development of a decentralized optimization framework which embeds the decomposition strategies in a distributed computing environment. The complexity study can provide fundamental guidelines to practical applications of the decomposition and coordination methods. In this thesis, several case studies imply the viability of the proposed decentralized optimization techniques for real industrial applications. A pulp mill benchmark problem is used to investigate the applicability of the LP/QP decentralized optimization strategies, while a truck allocation problem in the decision support of mining operations is used to study the MILP decentralized optimization strategies.

Cheng, Ruoyu

442

NASA Astrophysics Data System (ADS)

Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ˜15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ˜45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ˜35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging.

Karakatsanis, Nicolas A.; Lodge, Martin A.; Tahari, Abdel K.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman

2013-10-01

443

Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ~15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ~45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ~35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging. PMID:24080962

Karakatsanis, Nicolas A; Lodge, Martin A; Tahari, Abdel K; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

444

A dynamic optimization model of the diel vertical distribution of a pelagic planktivorous fish

NASA Astrophysics Data System (ADS)

A stochastic dynamic optimization model for the diel depth distribution of juveniles and adults of the mesopelagic planktivore Maurolicus muelleri (Gmelin) is developed and used for a winter situation. Observations from Masfjorden, western Norway, reveal differences in vertical distribution, growth and mortality between juveniles and adults in January. Juveniles stay within the upper 100m with high feeding rates, while adults stay within the 100-150m zone with very low feeding rates during the diel cycle. The difference in depth profitability is assumed to be caused by age-dependent processes, and are calculated from a mechanistic model for visual feeding. The environment is described as a set of habitats represented by discrete depth intervals along the vertical axis, differing with respect to light intensity, food abundance, predation risk and temperature. The short time interval (24h) allows fitness to be linearly related to growth (feeding), assuming that growth increases the future reproductive output of the fish. Optimal depth position is calculated from balancing feeding opportunity against mortality risk, where the fitness reward gained by feeding is weighted against the danger of being killed by a predator. A basic run is established, and the model is validated by comparing predictions and observations. The sensitivity for different parameter values is also tested. The modelled vertical distributions and feeding patterns of juvenile and adult fish correspond well with the observations, and the assumption of age differences in mortality-feeding trade-offs seems adequate to explain the different depth profitability of the two age groups. The results indicate a preference for crepuscular feeding activity of the juveniles, and the vertical distribution of zooplankton seems to be the most important environmental factor regulating the adult depth position during the winter months in Masfjorden.

Rosland, Rune; Giske, Jarl

445

Fuzzy multiobjective models for optimal operation of a hydropower system

NASA Astrophysics Data System (ADS)

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

2013-06-01

446

NASA Astrophysics Data System (ADS)

By using dynamic programming (DP) which is a kind of global optimization algorithm, an energy management control strategy for a parallel PHEV on different charging depleting range (CDR) had been studied. The results show that motor-dominant control strategy should be applied to the PHEV when CDR is less than 55km, and engine-dominant control strategy should be used when CDR is more than 55km. With optimal control strategies from DP, the best economic performance can be obtained as CDR is 55km; PHEV average equivalence fuel consumption can be reduced to 2.9L/100km which is 63% lower than that of prototype vehicle.

Yang, Shichun; Li, Ming; Cui, Haigang; Cao, Yaoguang; Wang, Gang; Lei, Qiang

447

We describe a method for the development of the optimal spatial distributions of the porosity phi and permeability k of a large-scale porous medium. The optimal distributions are constrained by static and dynamic data. The static data that we utilize are limited data for phi and k, which the method honors in the optimal model and utilizes their correlation functions in the optimization process. The dynamic data include the first-arrival (FA) times, at a number of receivers, of seismic waves that have propagated in the porous medium, and the time-dependent production rates of a fluid that flows in the medium. The method combines the simulated-annealing method with a simulator that solves numerically the three-dimensional (3D) acoustic wave equation and computes the FA times, and a second simulator that solves the 3D governing equation for the fluid's pressure as a function of time. To our knowledge, this is the first time that an optimization method has been developed to determine simultaneously the global minima of two distinct total energy functions. As a stringent test of the method's accuracy, we solve for flow of two immiscible fluids in the same porous medium, without using any data for the two-phase flow problem in the optimization process. We show that the optimal model, in addition to honoring the data, also yields accurate spatial distributions of phi and k, as well as providing accurate quantitative predictions for the single- and two-phase flow problems. The efficiency of the computations is discussed in detail. PMID:17677168

Hamzehpour, Hossein; Rasaei, M Reza; Sahimi, Muhammad

2007-05-01

448

A long-standing problem with econometric oil and gas supply models is the lack of a dynamic optimization framework that incorporates expectations of future prices and costs as a basis for the econometric equations. This dissertation attempts to remedy this problem by using a rational-expectations model of the United States oil market in which a representative competitive firm chooses an exploratory drilling plan so as to maximize the expected discounted net present value of oil discoveries. The quadratic objective function and linear laws of motion for exogenous variables lead to a linear exploratory drilling decision rule. This decision rule is then estimated by full-information maximum likelihood using US monthly data for the 1973 through 1985 period. The 1986 oil price collapse is assumed to change the way that the representative firm forms price expectations. This change is the price law of motion is incorporated into the model and future domestic exploratory drilling levels and crude oil discoveries are then forecasted.

Walls, M.A.

1988-01-01

449

Optimal Intrinsic Dynamics for Bursting in a Three-Cell Network

NASA Astrophysics Data System (ADS)

Previous numerical and analytical work has shown that synaptic coupling can allow a network of model neurons to synchronize despite heterogeneity in intrinsic parameter values. In particular, synchronous bursting oscillations can arise in a network with excitatory synaptic coupling, even in the absence of intrinsically bursting neurons. In this work, we explore how the intrinsic dynamics of neurons within a reduced three-cell network influence its ability to exhibit synchronous bursting and the frequency range over which such activity can occur. We establish necessary and sufficient conditions for the existence of synchronous bursting solutions and perform related numerical experiments in three-cell networks that include a quiescent cell, a tonically active cell, and a third added cell. Our results show that, in most cases, the addition of a quiescent cell is optimal for synchronous network bursting, in a variety of ways, and that intrinsically bursting cells can be detrimental to synchronous bursting, and we explain the mechanisms underlying these effects. These findings may help explain how robust synchronous oscillations arise in neuronal central pattern generators, such as the mammalian inspiratory network, despite the presence of significant cellular heterogeneity. They also support the idea that intrinsic burst capabilities of individual cells need not be central to these networks' rhythms.

Dunmyre, Justin R.; Rubin, Jonathan E.

2010-01-01

450

Optimization of a Continuous Hybrid Impeller Mixer via Computational Fluid Dynamics

This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-? turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. PMID:25170524

Othman, N.; Kamarudin, S. K.; Takriff, M. S.; Rosli, M. I.; Engku Chik, E. M. F.; Meor Adnan, M. A. K.

2014-01-01

451

Optimization of a continuous hybrid impeller mixer via computational fluid dynamics.

This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-? turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. PMID:25170524

Othman, N; Kamarudin, S K; Takriff, M S; Rosli, M I; Engku Chik, E M F; Meor Adnan, M A K

2014-01-01

452

Merging spatially variant physical process models under an optimized systems dynamics framework.

The complexity of water resource issues, its interconnectedness to other systems, and the involvement of competing stakeholders often overwhelm decision-makers and inhibit the creation of clear management strategies. While a range of modeling tools and procedures exist to address these problems, they tend to be case specific and generally emphasize either a quantitative and overly analytic approach or present a qualitative dialogue-based approach lacking the ability to fully explore consequences of different policy decisions. The integration of these two approaches is needed to drive toward final decisions and engender effective outcomes. Given these limitations, the Computer Assisted Dispute Resolution system (CADRe) was developed to aid in stakeholder inclusive resource planning. This modeling and negotiation system uniquely addresses resource concerns by developing a spatially varying system dynamics model as well as innovative global optimization search techniques to maximize outcomes from participatory dialogues. Ultimately, the core system architecture of CADRe also serves as the cornerstone upon which key scientific innovation and challenges can be addressed.

Cain, William O. (University of Texas at Austin, Austin, TX); Lowry, Thomas Stephen; Pierce, Suzanne A.; Tidwell, Vincent Carroll

2007-10-01

453

NASA Astrophysics Data System (ADS)

The MonALISA (Monitoring Agents in a Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information. Program summaryProgram title: MonALISA Catalogue identifier: AEEZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Caltech License - free for all non-commercial activities No. of lines in distributed program, including test data, etc.: 147 802 No. of bytes in distributed program, including test data, etc.: 2 5913 689 Distribution format: tar.gz Programming language: Java, additional APIs available in Java, C, C++, Perl and python Computer: Computing Clusters, Network Devices, Storage Systems, Large scale data intensive applications Operating system: The MonALISA service is mainly used in Linux, the MonALISA client runs on all major platforms (Windows, Linux, Solaris, MacOS). Has the code been vectorized or parallelized?: It is a multithreaded application. It will efficiently use all the available processors. RAM: for the MonALISA service the minimum required memory is 64 MB; if the JVM is started allocating more memory this will be used for internal caching. The MonALISA client requires typically 256-512 MB of memory. Classification: 6.5 External routines: Requires Java: JRE or JDK to run. These external packages are used (they are included in the distribution): JINI, JFreeChart, PostgreSQL (optional). Nature of problem: To monitor and control distributed computing clusters and grids, the network infrastructure, the storage systems, and the applications used on such facilities. The monitoring information gathered is used for developing the required higher level services, the components that provide decision support and some degree of automated decisions and for maintaining and optimizing workflow in large scale distributed systems. Solution method: The MonALISA framework is designed as an ensemble of autonomous self-describing agent-based subsystems which are registered as dynamic services. These services are able to collaborate and cooperate in performing a wide range of distributed information-gathering and processing tasks. Running time: MonALISA services are designed to run continuously to collect monitoring data and to trigger alarms or to take automatic actions in case it is necessary. References:http://monalisa.caltech.edu.

Legrand, I.; Newman, H.; Voicu, R.; Cirstoiu, C.; Grigoras, C.; Dobre, C.; Muraru, A.; Costan, A.; Dediu, M.; Stratan, C.

2009-12-01

454

Optimal Regulation of Heating Systems with Metering Based on Dynamic Simulation

the radiator's parameters. The primary purpose of these works is the strategy of optimal regulation of heating system with metering. In a ramiform heating system with three heat users, an optimal scheme with certain combination of different object functions...

Zhao, H.; Wang, P.; Zeng, G.; Tian, Y.

2006-01-01

455

Combined optimization model for sustainable energization strategy

NASA Astrophysics Data System (ADS)

Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.

Abtew, Mohammed Seid

456

Optimal Passive Dynamics for Torque/Force Control Kevin Kemper, Devin Koepl and Jonathan Hurst

Dynamic Robotics Laboratory Oregon State University Corvallis, Oregon drl@oregonstate.com Abstract & Manufacturing Engineering at Oregon State University. Passive dynamics are not always harmful. As an example

Hurst, Jonathan

457

NASA Technical Reports Server (NTRS)

This paper describes a finite volume computational thermo-fluid dynamics method to solve for Navier-Stokes equations in conjunction with energy equation and thermodynamic equation of state in an unstructured coordinate system. The system of equations have been solved by a simultaneous Newton-Raphson method and compared with several benchmark solutions. Excellent agreements have been obtained in each case and the method has been found to be significantly faster than conventional Computational Fluid Dynamic(CFD) methods and therefore has the potential for implementation in Multi-Disciplinary analysis and design optimization in fluid and thermal systems. The paper also describes an algorithm of design optimization based on Newton-Raphson method which has been recently tested in a turbomachinery application.

Majumdar, Alok; Schallhorn, Paul

1998-01-01

458

Dynamic optimal sliding-mode control for six-DOF follow-up robust tracking of active satellite

NASA Astrophysics Data System (ADS)

This paper presents a six-DOF follow-up tracking scheme for active target satellite tracking. The scheme is mainly composed of a robust tracking algorithm and a six-DOF follow-up control law. Firstly, a relative motion model using osculating reference orbit (ORO) is built and applied to the redundant adaptive robust extended Kalman filter (RAREKF) to form an ORO-based robust method as the tracking algorithm. Then, a dynamic optimal sliding-mode control (DOSMC) with dynamic optimal sliding surface (DOSS) is proposed to design the six-DOF follow-up control of both relative orbit and chaser attitude. The scheme structure is also discussed in the paper. Three cases are simulated to illustrate the advantage of ORO-based RAREKF and DOSMC and to verify the effectiveness of the presented follow-up tracking scheme.

Yuankai, Li; Zhongliang, Jing; Shiqiang, Hu

2011-09-01

459

This paper illustrates a method for choosing the optimal mix of wind capacity at several geographically dispersed locations. The method is based on a dynamic fuzzy search algorithm that can be applied to different optimization targets. We illustrate the method using two objective functions for the optimization: maximum economic benefit and maximum reliability. We also illustrate the sensitivity of the fuzzy economic benefit solutions to small perturbations of the capacity selections at each wind site. We find that small changes in site capacity and/or location have small effects on the economic benefit provided by wind power plants. We use electric load and generator data from Iowa, along with high-quality wind-speed data collected by the Iowa Wind Energy Institute.

Milligan, M. R., National Renewable Energy Laboratory; Factor, T., Iowa Wind Energy Institute

2001-09-21

460

NASA Astrophysics Data System (ADS)

A novel evolutionary algorithm called dynamic multi-swarm particle swarm optimizer (DMS-PSO) is used to improve the performance of fiber Bragg grating (FBG) sensors in a wavelength division multiplexed (WDM) network. Simulation results show that the root-mean-square (RMS) value of the Bragg wavelength detection error is 0.8pm when 10 FBGs in the WDM network are completely overlapped in the noisy environment.

Liang, J. J.; Chan, C. C.; Huang, V. L.; Suganthan, P. N.

2005-11-01

461

generation adequacy in a multi- area power system [2] uses an optimization procedure along with MARS is obtained from several runs of MARS. In a single MARS run, the outage of each component in the system are continuous variables. Mixed-integer programming and dynamic programming [9] were proposed to incorporate

462

Heuristic decomposition approach to buffer operation planning in a production process

Optimal operation planning or a production process with buffers and electric power plants is considered. The problem is formulated as a mixed-integer linear program with special structure. A suboptimal solution is obtained by a heuristic decomposition procedure. The master program is solved by the dynamic programming technique, and the subproblems are solved by a greedy method. When applied to the

NOBUO SANNOMIYA; CHISAKU ONISHI; Hiroyuki Yoshino

1990-01-01

463

NASA Astrophysics Data System (ADS)

Calibrating complex hydrological models faces two major challenges: firstly, extended models, especially when spatially distributed, encompass a large number of parameters with different (and possibly a-priori unknown) sensitivity. Due to the usually rough surface of the objective function, this aggravates the risk of an algorithm to converge in a local optimum. Thus, gradient-based optimization methods are often bound to fail without a very good prior estimate. Secondly, despite growing computational power, it is not uncommon that models of large extent in space or time take several minutes to run, which severely restricts the total number of model evaluations under given computational and time resources. While various heuristic methods successfully address the first challenge, they tend to conflict with the second challenge due to the increased number of evaluations necessary. In that context we analyzed three methods (Dynamically Dimensioned Search / DDS, Particle Swarm Optimization / PSO, Genetic Algorithms /GA). We performed tests with common "synthetic" objective functions and a calibration of the hydrological model WASA-SED with different number of parameters. When looking at the reduction of the objective function within few (i.e.< 1000) evaluations, the methods generally perform in the order (best to worst) DDS-PSO-GA. Only at a larger number, GA can excel. To speed up optimization, we executed DDS and PSO as parallel applications, i.e. using multiple CPUs and/or computers. The parallelisation has been implemented in the ppso-package for the free computation environment R. Special focus has been laid onto the options to resume interrupted optimization runs and visualize progress.

Francke, Till; Bronster, Axel; Shoemaker, Christine A.

2010-05-01

464

NASA Astrophysics Data System (ADS)

Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.

Peralta, Richard C.; Forghani, Ali; Fayad, Hala

2014-04-01

465

\\u000a Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This paper\\u000a describes the synthesis method of linear array geometry with minimum side lobe level and null control by the Dynamic Multi-Swarm\\u000a Particle Swarm Optimizer with Local Search (DMSPSO) which optimizes the spacing between the elements of the linear array to\\u000a produce a radiation

Pradipta Ghosh; Hamim Zafar

2010-01-01

466

Fast Dynamic Optimization of Robot Paths under Actuator Limits and Frictional Contact

are those of the author and do not necessarily reflect the views of DARPA. 1School of Informatics optimization requires the solution to high-dimensional, nonlinear optimization problems, which with uniform rate at 5 s duration, the robot falls over. Row 2: executed with uniform rate at 10 s duration

Hauser, Kris

467

An optimal foraging-based model of hunter-gatherer population dynamics

Population changes for hunter-gatherers are modeled on the basis of nutritional intake, which is determined using an optimal foraging model based upon the optimization technique of linear programming. The population model not only demonstrates how hunter-gatherer demography changes with nutrition, but also shows how their density influences food abundance in the environment which in turn affects their nutritional status. Differences

GARY E. BELOVSKY

1988-01-01

468

Dynamic online optimization of a house heating system in a fluctuating energy price

like wind turbine and photovoltaic parks. Although efficiency-wise attractive, these alternative en building heating system optimization where the goal is the minimization of energy costs, which in turn of the energy consumption in a building with energy storage capabilities. The goal is to find optimal policies

Skogestad, Sigurd

469

A robust optimization model for dynamic market with uncertain production cost

This article presents a robust optimization formulation for dealing with production cost uncertainty in an oligopolistic market scenario. It is not uncommon that players in the market face an equilibrium selling price but uncertain production costs. We show that, based on a nominal problem, the robust optimization formulation can be derived as a variational inequality with control and state variables.

Ming Wang; Kin Keung Lai; Stephen C. H. Leung; Ning Shi

2012-01-01

470

We describe an optimization strategy for minimizing total power consumption using dual threshold voltage (Vth) technology. Significant power savings are possible by simultaneous assignment of Vth with gate sizing. We propose an efficient algorithm based on linear programming that jointly performs Vth assignment and gate sizing to minimize total power under delay constraints. First, linear programming assigns the optimal amounts

David Nguyen; Abhijit Davare; Michael Orshansky; David G. Chinnery; Brandon Thompson; Kurt Keutzer

2003-01-01

471

A Novel Approach to Dynamic Optimization of ODE and DAE Systems as High-Index Problems

Solution of many problems in plant operations requires determination of optimal control profi les subject to state constraints for systems modeled by ordinary differential equations (ODEs) or dif ferential-alge- braic equations (DAEs). For example, optimal temperature and\\/or feed rate profiles are important for the oper - ation of many batch reactions. Similar observations apply to reflux policies for batch distillation,

William F. Feehery; Julio R. Banga; Paul I. Barton

472

NASA Astrophysics Data System (ADS)

We study the issue of quality assessment in tone mapping-based high-dynamic-range (HDR) image compression. In this, there are two stages at which a decision should be made regarding perceptual visual quality: (a) for finding the optimal parameters of the dynamic range reduction function so that the visual quality is maximized, and (b) visual quality judgment of the decompressed image. We first investigate two objective optimization criteria, namely mean squared error and structural similarity index measure, toward optimization of a tone mapping model-based HDR image compression method. We then conduct a comprehensive subjective study to evaluate the visual quality of the compressed HDR images. Therefore, we consider both objective and subjective aspects for HDR image compression. To our knowledge, no systematic and comprehensive studies exist in the current literature which shed light on the issue of quality assessment in HDR compression. So this study brings in new knowledge and perspective for the relatively less investigated topic of HDR compression from the view point of perceptual quality. We further evaluate the prediction performances of four objective methods on the 140 compressed HDR images that have been subjectively rated.

Narwaria, Manish; Da Silva, Matthieu Perreira; Le Callet, Patrick; Pepion, Romuald

2013-10-01

473

Background A key step in any process that converts lignocellulose to biofuels is the efficient fermentation of both hexose and pentose sugars. The co-culture of respiratory-deficient Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis has been identified as a promising system for microaerobic ethanol production because S. cerevisiae only consumes glucose while S. stipitis efficiently converts xylose to ethanol. Results To better predict how these two yeasts behave in batch co-culture and to optimize system performance, a dynamic flux balance model describing co-culture metabolism was developed from genome-scale metabolic reconstructions of the individual organisms. First a dynamic model was developed for each organism by estimating substrate uptake kinetic parameters from batch pure culture data and evaluating model extensibility to different microaerobic growth conditions. The co-culture model was constructed by combining the two individual models assuming a cellular objective of total growth rate maximization. To obtain accurate predictions of batch co-culture data collected at different microaerobic conditions, the S. cerevisiae maximum glucose uptake rate was reduced from its pure culture value to account for more efficient S. stipitis glucose uptake in co-culture. The dynamic co-culture model was used to predict the inoculum concentration and aeration level that maximized batch ethanol productivity. The model predictions were validated with batch co-culture experiments performed at the optimal conditions. Furthermore, the dynamic model was used to predict how engineered improvements to the S. stipitis xylose transport system could improve co-culture ethanol production. Conclusions These results demonstrate the utility of the dynamic co-culture metabolic model for guiding process and metabolic engineering efforts aimed at increasing microaerobic ethanol production from glucose/xylose mixtures. PMID:23548183

2013-01-01

474

A Near-Optimal Solution Method of Multi-Item Multi-Process Dynamic Lot Size Scheduling Problem

NASA Astrophysics Data System (ADS)

This paper addresses a multi-item multi-process dynamic lot size scheduling problem with general product structure and setup time. In this problem, there exist various heterogeneous decision features such as lot sizing, lot sequencing, dispatching, and so on. We present a near-optimal solution method, which we call a narrow sense Lagrangian decomposition coordination method of solving all decision features involved in this problem simultaneously without specifying or awaking to them one by one. First, splitting the planning horizon into very small time-slots, for any item on any machine at any timeslot we denote a state of processing by using a binary decision variable which takes a value of unity if it is processed, and else then zero. Second, dealing with the transition of the inventory state of each item and time transition of each setup explicitly, we formulate the problem into a multi-dimensional dynamic optimization problem with constraints. Third, paying attention to the existence of the interaction constraints, we decompose the whole problem into item-based sub problems that can be reformulated into dynamic programming of one dimension to dissolve the curse of dimensionality. At the aim of guaranteeing the decomposability, we formulate the problem by echelon inventory. The computational procedure consists of solving sub problems for given Lagrange multiplier values and of coordinating those values. Finally, we verify the presented method by using a numerical model.

Muramatsu, Kenji; Warman, Aditya; Kobayashi, Minoru

475

(PACT), Sept. 2005. An Event-Driven Multithreaded Dynamic Optimization Framework Weifeng Zhang Brad of execution, either through chip multipro- cessing [18], hardware multithreading [37], or a combina- tion [22. Our framework focuses on efficient dynamic optimiza- tion for a multithreaded processor

Wang, Deli

476

NASA Astrophysics Data System (ADS)

In this paper we report on the development of a dynamic MATLAB SIMULINK® model for the water and electrolyte balance inside the human body. This model is part of an environmentally sensitive dynamic human model for the optimization and verification of environmental control and life support systems (ECLSS) in space flight applications. An ECLSS provides all vital supplies for supporting human life on board a spacecraft. As human space flight today focuses on medium- to long-term missions, the strategy in ECLSS is shifting to closed loop systems. For these systems the dynamic stability and function over long duration are essential. However, the only evaluation and rating methods for ECLSS up to now are either expensive trial and error breadboarding strategies or static and semi-dynamic simulations. In order to overcome this mismatch the Exploration Group at Technische Universität München (TUM) is developing a dynamic environmental simulation, the "Virtual Habitat" (V-HAB). The central element of this simulation is the dynamic and environmentally sensitive human model. The water subsystem simulation of the human model discussed in this paper is of vital importance for the efficiency of possible ECLSS optimizations, as an over- or under-scaled water subsystem would have an adverse effect on the overall mass budget. On the other hand water has a pivotal role in the human organism. Water accounts for about 60% of the total body mass and is educt and product of numerous metabolic reactions. It is a transport medium for solutes and, due to its high evaporation enthalpy, provides the most potent medium for heat load dissipation. In a system engineering approach the human water balance was worked out by simulating the human body's subsystems and their interactions. The body fluids were assumed to reside in three compartments: blood plasma, interstitial fluid and intracellular fluid. In addition, the active and passive transport of water and solutes between those compartments was modeled dynamically. A kidney model regulates the electrolyte concentration in body fluids (osmolality) in narrow confines and a thirst mechanism models the urge to ingest water. A controlled exchange of water and electrolytes with other human subsystems, as well as with the environment, is implemented. Finally, the changes in body composition due to muscle growth are accounted for. The outcome of this is a dynamic water and electrolyte balance, which is capable of representing body reactions like thirst and headaches, as well as heat stroke and collapse, as a response to its work load and environment.

Hager, P.; Czupalla, M.; Walter, U.

2010-11-01

477

No matching vaccine is immediately available when a novel influenza strain breaks out. Several nonvaccine-related strategies must be employed to control an influenza epidemic, including antiviral treatment, patient isolation, and immigration detection. This paper presents the development and application of two regional dynamic models of influenza with Pontryagin’s Maximum Principle to determine the optimal control strategies for an epidemic and the corresponding minimum antiviral stockpiles. Antiviral treatment was found to be the most effective measure to control new influenza outbreaks. In the case of inadequate antiviral resources, the preferred approach was the centralized use of antiviral resources in the early stage of the epidemic. Immigration detection was the least cost-effective; however, when used in combination with the other measures, it may play a larger role. The reasonable mix of the three control measures could reduce the number of clinical cases substantially, to achieve the optimal control of new influenza. PMID:24392151

Zhang, Wen-Dou; Zu, Zheng-Hu; Xu, Qing; Xu, Zhi-Jing; Liu, Jin-Jie; Zheng, Tao

2014-01-01

478

This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles' activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem. PMID:24851085

Li, Desheng

2014-01-01

479

A Parallel General Purpose Mulit-Objective Optimization Framework, with Application to Beam Dynamics

Particle accelerators are invaluable tools for research in the basic and applied sciences, in fields such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. We propose to tackle this problem by means of multi-objective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame we require a fast and scalable software framework. In this paper, we present the implementation of such a general-purpose framework for simulation based multi-objective optimization methods that allows the automatic investigation of optimal sets of machine parameters. The implementation is based on a master/slave paradigm, employing several masters that govern a set of slaves executing simulations and performing optimization task...

Ineichen, Y; Kolano, A; Bekas, C; Curioni, A; Arbenz, P

2013-01-01