Simulation-based optimization for design parameter exploration in hybrid system: a defense system example

This paper presents a method for solving the optimization problems that arise in hybrid systems. These systems are characterized by a combination of continuous and discrete event systems. The proposed method aims to find optimal design configurations that satisfy a goal performance. For exploring design parameter space, the proposed method integrates a metamodel and a metaheuristic method. The role of the metamodel is to give good initial candidates and reduced search space to the metaheuristic optimizer. On the other hand, the metaheuristic method improves the quality of the given candidates. This proposal also demonstrates a defense system that illustrates the practical application of the presented method. The optimization objective of the case study is to find the required operational capability configurations of a decoy that meet the desired measure of effectiveness. Through a comparison with a full search method, two metamodeling methods without the aid of metaheuristics and a metaheuristic method without the support of metamodels, we confirmed that the proposed method provides same high-quality solutions as those of the full search method at a small computational cost.

[1]  Chang Ho Sung,et al.  Interoperation of DEVS models and differential equation models using HLA/RTI: hybrid simulation of engineering and engagement level models , 2009, SpringSim '09.

[3]  Raid Al-Aomar,et al.  General methodology 1: a robust simulation-based multicriteria optimization methodology , 2002, WSC '02.

[4]  Francesco Longo,et al.  An advanced supply chain management tool based on modeling and simulation , 2008, Comput. Ind. Eng..

[5]  Lalit K Piplani.  Systems Acquisition Manager's Guide for the Use of Models and Simulations , 1994 .

[6]  Peter Köchel,et al.  Simulation-based sequencing and lot size optimisation for a production-and-inventory system with multiple items , 2006 .

[7]  Peter Köchel,et al.  Kanban optimization by simulation and evolution , 2002 .

[8]  Michel Bierlaire,et al.  A simulation-based optimization framework for urban traffic control , 2011 .

[9]  Galina Merkuryeva.  METAMODELLING FOR SIMULATION APPLICATIONS IN PRODUCTION AND LOGISTICS , 2004 .

[10]  Fang Zhao,et al.  Optimization of transit route network, vehicle headways and timetables for large-scale transit networks , 2008, Eur. J. Oper. Res..

[11]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[12]  Alfredo Vellido,et al.  Neural networks in business: a survey of applications (1992–1998) , 1999 .

[13]  Roger McHaney.  Integration of the Genetic Algorithm and Discrete-Event Computer Simulation for Decision Support , 1999, Simul..

[14]  P.J. Antsaklis,et al.  Supervisory control of hybrid systems , 2000, Proceedings of the IEEE.

[15]  Fang Zhao,et al.  Optimization of transit network layout and headway with a combined genetic algorithm and simulated annealing method , 2006 .

[16]  Fred W. Glover,et al.  Simulation-based optimization: practical introduction to simulation optimization , 2003, WSC '03.

[17]  Nanxin Wang,et al.  BACKWARD MAPPING METHODOLOGY FOR DESIGN SYNTHESIS , 1999 .

[18]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[19]  Marcus Andersson,et al.  Metamodel-Assisted Simulation-Based Optimization of a Real-World Manufacturing Problem , 2007 .

[20]  Kenny Q. Ye.  Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments , 1998 .

[21]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[22]  Miquel Angel Piera Eroles,et al.  Optimization of Logistic and Manufacturing Systems through Simulation: A Colored Petri Net-Based Methodology , 2004, Simul..

[23]  Taho Yang,et al.  Design of manufacturing systems by a hybrid approach with neural network metamodelling and stochastic local search , 2002 .

[24]  Luciano Lamberti,et al.  An efficient simulated annealing algorithm for design optimization of truss structures , 2008 .

[25]  Souran Manoochehri,et al.  GENERATING OPTIMAL CONFIGURATIONS IN STRUCTURAL DESIGN USING SIMULATED ANNEALING , 1997 .

[26]  Russell R. Barton,et al.  Chapter 18 Metamodel-Based Simulation Optimization , 2006, Simulation.

[27]  Basílio E. A. Milani,et al.  Generation of optimal schedules for metro lines using model predictive control , 2004, Autom..

[28]  Hyunbo Cho,et al.  Hybrid algorithm for discrete event simulation based supply chain optimization , 2010, Expert Syst. Appl..

[29]  Özgür Yalçinkaya,et al.  Modelling and optimization of average travel time for a metro line by simulation and response surface methodology , 2009, Eur. J. Oper. Res..

[30]  R. Al-Aomar,et al.  A robust simulation-based multicriteria optimization methodology , 2002, Proceedings of the Winter Simulation Conference.

[31]  Peter Köchel,et al.  Optimal control of a distributed service system with moving resources: Application to the fleet sizing and allocation problem , 2003 .

[32]  Paul I. Barton,et al.  Modeling, simulation, sensitivity analysis, and optimization of hybrid systems , 2002, TOMC.

[33]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[34]  Julio R. Banga,et al.  Optimization of hybrid discrete/continuous dynamic systems , 2000 .

[35]  Russell R. Barton,et al.  Issues in development of simultaneous forward-inverse metamodels , 2005, Proceedings of the Winter Simulation Conference, 2005..

[36]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[37]  Mehdi Ghatee,et al.  An expert system for network control problems and its applications in large scale network design under uncertainty , 2009 .

[38]  Robert Ivor John,et al.  A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[39]  Tag Gon Kim,et al.  Interoperation between Engagement-and Engineering-level Models for Effectiveness Analyses , 2011 .

[40]  Egill Másson,et al.  Introduction to computation and learning in artificial neural networks , 1990 .

[41]  S. H. Huang,et al.  Artificial neural networks in manufacturing: concepts, applications, and perspectives , 1994 .

[42]  Yangsheng Xu,et al.  Optimal Design for Urban Mass Transit Network Based on Evolutionary Algorithms , 2005, ICNC.

[43]  Amos H. C. Ng,et al.  Metamodel-Assisted Global Search Using a Probing Technique , 2007, IMECS.

[44]  Chang Ho Sung,et al.  Framework for Simulation of Hybrid Systems: Interoperation of Discrete Event and Continuous Simulators Using HLA/RTI , 2011, 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation.

[45]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[46]  Luis Puigjaner,et al.  A simulation-based optimization framework for parameter optimization of supply-chain networks , 2006 .

[47]  Kyung-Min Seo,et al.  Measurement of Effectiveness for an Anti-torpedo Combat System Using a Discrete Event Systems Specification-based Underwater Warfare Simulator , 2011 .

[48]  Chris P. Pantelides,et al.  Modified iterated simulated annealing algorithm for structural synthesis , 2000 .

[49]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[50]  Thomas W. Lucas,et al.  Efficient Nearly Orthogonal and Space-Filling Latin Hypercubes , 2007, Technometrics.

[51]  Galina Merkuryeva,et al.  Simulation-based planning and optimization in multi-echelon supply chains , 2011, Simul..

[52]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[53]  J. Frederick Klingener.  Programming combined discrete-continuous simulation models for performance , 1996, Winter Simulation Conference.

[54]  Amos H. C. Ng,et al.  Simulation-based optimisation using local search and neural network metamodels , 2006, Artificial Intelligence and Soft Computing.

[55]  T. M. Cioppa,et al.  Efficient nearly orthogonal and space-filling experimental designs for high-dimensional complex models , 2002 .