A comprehensive literature classification of simulation optimisation methods

Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measure

[1]  Peter W. Glynn,et al.  Likelihood Ratio Sensitivity Analysis for Markovian Models of Highly Dependable Systems , 1994, Oper. Res..

[2]  L. W. Schruben,et al.  Simulation sensitivity analysis using frequency domain experiments , 1989, WSC '89.

[3]  Lee W. Schruben,et al.  A survey of simulation optimization techniques and procedures , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[4]  M. Nakayama Multiple comparisons with the best using common random numbers for steady-state simulations , 2000 .

[5]  Connie M. Borror,et al.  Response Surface Methodology: A Retrospective and Literature Survey , 2004 .

[6]  Marvin K. Nakayama,et al.  Multiple-comparison procedures for steady-state simulations , 1997 .

[7]  Barry L. Nelson,et al.  Batch-size effects on simulation optimization using multiple comparisons with the best , 1990, 1990 Winter Simulation Conference Proceedings.

[8]  S. Andradóttir Optimization of the transient and steady state behavior of discrete event systems , 1996 .

[9]  Yu-Chi Ho,et al.  Ordinal optimization approach to rare event probability problems , 1995, Discret. Event Dyn. Syst..

[10]  Peter Köchel,et al.  Simulation-based optimisation of multi-echelon inventory systems , 2005 .

[11]  Marvin K. Nakayama,et al.  Two-stage procedures for multiple comparisons with a control in steady-state simulations , 1996, Winter Simulation Conference.

[12]  Farhad Azadivar,et al.  A tutorial on simulation optimization , 1992, WSC '92.

[13]  Barry L. Nelson,et al.  Selecting the best system: theory and methods , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

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

[15]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[16]  T. Simpson,et al.  Computationally Inexpensive Metamodel Assessment Strategies , 2002 .

[17]  A. Law,et al.  A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation , 1985 .

[18]  Heinz Mühlenbein,et al.  6. Genetic algorithms , 2003 .

[19]  Mahmoud H. Alrefaei,et al.  A simulated annealing technique for multi-objective simulation optimization , 2009, Appl. Math. Comput..

[20]  Enver Yücesan,et al.  Computational issues for accessibility in discrete event simulation , 1996, TOMC.

[21]  Richard W. Eglese,et al.  Simulated annealing: A tool for operational research , 1990 .

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

[23]  E. Chong,et al.  Optimization of queues using an infinitesimal perturbation analysis-based stochastic algorithm with general update times , 1993 .

[24]  Douglas J. Morrice,et al.  A simulation clock-based solution to the frequency domain experiment indexing problem , 1997 .

[25]  T. W. E. Lau,et al.  Universal Alignment Probabilities and Subset Selection for Ordinal Optimization , 1997 .

[26]  C. Richard Cassady,et al.  Combining preventive maintenance and statistical process control: a preliminary investigation , 2000 .

[27]  Yu-Chi Ho,et al.  Iterative ordinal optimization and its applications , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[28]  Stephen M. Robinson,et al.  Analysis of Sample-Path Optimization , 1996, Math. Oper. Res..

[29]  Russell R. Barton,et al.  Metamodels for simulation input-output relations , 1992, WSC '92.

[30]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[31]  R. Rubinstein How to optimize discrete-event systems from a single sample path by the score function method , 1991 .

[32]  Sigrún Andradóttir,et al.  A review of simulation optimization techniques , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[33]  Robert G. Sargent,et al.  Gaussian radial basis functions for simulation metamodeling , 2002, Proceedings of the Winter Simulation Conference.

[34]  R. Franke Scattered data interpolation: tests of some methods , 1982 .

[35]  Barry L. Nelson,et al.  Multiple comparisons with the best for steady-state simulation , 1993, TOMC.

[36]  James R. Wilson,et al.  A revised simplex search procedure for stochastic simulation response-surface optimization , 1998, WSC '98.

[37]  Gunar E. Liepins,et al.  Genetic algorithms: Foundations and applications , 1990 .

[38]  Catherine M. Harmonosky,et al.  A simulation optimization method that considers uncertainty and multiple performance measures , 2007, Eur. J. Oper. Res..

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

[40]  Alexandre Dolgui,et al.  A stochastic method for discrete and continuous optimization in manufacturing systems , 1997, J. Intell. Manuf..

[41]  P. Glynn Optimization of stochastic systems via simulation , 1989, WSC '89.

[42]  Leyuan Shi,et al.  An Integrated Framework For Deterministic And Stochastic Optimization , 1997, Winter Simulation Conference Proceedings,.

[43]  Tom Brady,et al.  Heuristic optimization using computer simulation: a study of strong levels in a pharmaceutical manufacturing laboratory , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[44]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

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

[46]  Jack P. C. Kleijnen,et al.  An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis , 2005, Eur. J. Oper. Res..

[47]  Ulrich Dieter,et al.  Discrete Optimization Approximating k-cuts using network strength as a Lagrangean relaxation , 2000 .

[48]  R. H. Myers,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[49]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[50]  R. Tibshirani,et al.  The II P method for estimating multivariate functions from noisy data , 1991 .

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

[52]  F. Masmoudi,et al.  A multi-product lot size in make-to-order supply chain using discrete event simulation and response surface methodology , 2010 .

[53]  Ray J. Paul,et al.  Simulation optimization with the linear move and exchange move optimization algorithm , 1999, WSC '99.

[54]  John C. Butler,et al.  Sensitivity analysis in ranking and selection for multiple performance measures , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[55]  Yu-Chi Ho,et al.  Effect of correlated estimation errors in ordinal optimization , 1992, WSC '92.

[56]  Mark L. Spearman,et al.  Improving the design of stochastic production lines: an approach using perturbation analysis , 1993 .

[57]  Alireza Kabirian,et al.  Allocation of simulation runs for simulation optimization , 2007, 2007 Winter Simulation Conference.

[58]  Fred W. Glover,et al.  Simulation optimization: a review, new developments, and applications , 2005, Proceedings of the Winter Simulation Conference, 2005..

[59]  P. Glynn LIKELIHOOD RATIO GRADIENT ESTIMATION : AN OVERVIEW by , 2022 .

[60]  Liyi Dai Perturbation analysis via coupling , 2000, IEEE Trans. Autom. Control..

[61]  Lee W. Schruben,et al.  Simulation sensitivity analysis: A frequency domain approach , 1981, WSC '81.

[62]  Marvin K. Nakayama,et al.  Two-stage multiple-comparison procedures for steady-state simulations , 1999, TOMC.

[63]  Jürgen Branke,et al.  Selecting a Selection Procedure , 2007, Manag. Sci..

[64]  Michael C. Fu,et al.  Optimization via simulation: A review , 1994, Ann. Oper. Res..

[65]  Xiaolan Xie Dynamics and convergence rate of ordinal comparison of stochastic discrete-event systems , 1997, IEEE Trans. Autom. Control..

[66]  Peter W. Glynn,et al.  Likelilood ratio gradient estimation: an overview , 1987, WSC '87.

[67]  William Casey Howell,et al.  Simulation optimization of traffic light signal timings via perturbation analysis , 2006 .

[68]  Leyuan Shi,et al.  Simultaneous simulation experiments and nested partition for discrete resource allocation in supply chain management , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[69]  Bernd Heidergott,et al.  Sensitivity analysis of a manufacturing workstation using perturbation analysis techniques , 1995 .

[70]  Russell R. Barton,et al.  Sample size selection for improved Nelder-Mead performance , 1995, WSC '95.

[71]  Barry L. Nelson,et al.  Statistical screening, selection, and multiple comparison procedures in computer simulation , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[72]  M. C. Jones,et al.  Spline Smoothing and Nonparametric Regression. , 1989 .

[73]  Douglas J. Morrice,et al.  An approach to ranking and selection for multiple performance measures , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[74]  Ray J. Paul,et al.  Simulation optimisation using a genetic algorithm , 1998, Simul. Pract. Theory.

[75]  A. Patera,et al.  ICASE Report No . 93-50 191510 IC S 2 O Years ofExcellence SURROGATES FOR NUMERICAL SIMULATIONS ; OPTIMIZATION OF EDDY-PROMOTER HEAT EXCHANGERS , 1993 .

[76]  M. Fu,et al.  Efficient Design and Sensitivity Analysis of Control Charts Using Monte Carlo Simulation , 1999 .

[77]  A. Maria,et al.  Simulation Optimization: Methods And Applications , 1997, Winter Simulation Conference Proceedings,.

[78]  Robert W. Blanning,et al.  The construction and implementation of metamodels , 1975 .

[79]  P. W. Glynn Likelihood ratio derviative estimators for stochastic systems , 1989, WSC '89.

[80]  J. Neher A problem of multiple comparisons , 2011 .

[81]  J. Kleijnen Statistical tools for simulation practitioners , 1986 .

[82]  Christine M. Anderson-Cook Practical Genetic Algorithms (2nd ed.): Randy L. Haupt and Sue Ellen Haupt , 2005 .

[83]  Chun-Hung Chen,et al.  An effective approach to smartly allocate computing budget for discrete event simulation , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[84]  Masao Fukushima,et al.  Tabu Search directed by direct search methods for nonlinear global optimization , 2006, Eur. J. Oper. Res..

[85]  J. Mittenthal,et al.  Optimization of an automated manufacturing system simulation model using simulated annealing , 1989, WSC '89.

[86]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[87]  Mark Fleischer Simulated annealing: past, present, and future , 1995, WSC '95.

[88]  Minghe Sun,et al.  Determining buffer location and size in production lines using tabu search , 1998, Eur. J. Oper. Res..

[89]  Timothy M. Mauery,et al.  COMPARISON OF RESPONSE SURFACE AND KRIGING MODELS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION , 1998 .

[90]  L. Schruben,et al.  Designing simultaneous simulation experiments , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[91]  Lee W. Schruben,et al.  Retrospective simulation response optimization , 1991, 1991 Winter Simulation Conference Proceedings..

[92]  Chun-Hung Chen A lower bound for the correct subset-selection probability and its application to discrete-event system simulations , 1996, IEEE Trans. Autom. Control..

[93]  Loo Hay Lee,et al.  Explanation of goal softening in ordinal optimization , 1999, IEEE Trans. Autom. Control..

[94]  Enrique Rotstein,et al.  Simulation and Optimization , 1997 .

[95]  Barry L. Nelson,et al.  Two-Stage Multiple Comparisons with the Best for Computer Simulation , 1995, Oper. Res..

[96]  A. Maria Genetic algorithm for multimodal continuous optimization problems , 1995 .

[97]  Barry L. Nelson,et al.  Optimization over a finite number of system designs with one-stage sampling and multiple comparisons with the best , 1988, WSC '88.

[98]  A. Shapiro,et al.  Convergence analysis of gradient descent stochastic algorithms , 1996 .

[99]  Yu-Chi Ho Overview of ordinal optimization , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[100]  Leyuan Shi,et al.  Some topics for simulation optimization , 2008, 2008 Winter Simulation Conference.

[101]  Julie L. Swann,et al.  Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large , 2001, Oper. Res..

[102]  G. Tompkins,et al.  Genetic algorithms in optimizing simulated systems , 1995, Winter Simulation Conference Proceedings, 1995..

[103]  Leo Breiman,et al.  [The ∏ Method for Estimating Multivariate Functions from Noisy Data]: Response , 1991 .

[104]  George Ch. Pflug,et al.  Optimization of Stochastic Models , 1996 .

[105]  G. J. Lewis,et al.  Two-Stage Procedures for Multiple Comparisons with a Control , 1992 .

[106]  M. Fu What you should know about simulation and derivatives , 2008 .

[107]  B.W.M. Bettonvil A formal description of discrete event dynamic systems including infinitesimal perturbation analysis , 1989 .

[108]  Yu-Chi Ho,et al.  Ordinal optimization of DEDS , 1992, Discret. Event Dyn. Syst..

[109]  Jack P. C. Kleijnen,et al.  Response surface methodology for constrained simulation optimization: An overview , 2008, Simul. Model. Pract. Theory.

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

[111]  L. Schruben,et al.  Simulation Optimization Using Simultaneous Replications And Event Time Dilation , 1997, Winter Simulation Conference Proceedings,.

[112]  Christos Koulamas,et al.  A survey of simulated annealing applications to operations research problems , 1994 .

[113]  Sigurdur Ólafsson,et al.  Towards a framework for black-box simulation optimization , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[114]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[115]  Max D. Morris,et al.  The spatial correlation function approach to response surface estimation , 1992, WSC '92.

[116]  Jack P. C. Kleijnen Sensitivity analysis and optimization in simulation: design of experiments and case studies , 1995, WSC '95.

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

[118]  Fong-Ching Yuan Simulation-optimization mechanism for expansion strategy using real option theory , 2009, Expert Syst. Appl..

[119]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[120]  Lee W. Schruben,et al.  A survey of recent advances in discrete input parameter discrete-event simulation optimization , 2004 .

[121]  J. S. Ivey,et al.  Nelder-Mead simplex modifications for simulation optimization , 1996 .

[122]  Yogesh Jaluria,et al.  Simulation-based optimization of thermal systems , 2009 .

[123]  Jorge Haddock,et al.  Simulation optimization using simulated annealing , 1992 .

[124]  Jack P. C. Kleijnen Design and Analysis of Simulation Experiments , 2007 .

[125]  Cigdem Alabas-Uslu,et al.  Simulation optimization using tabu search , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[126]  Sujin Kim,et al.  Gradient-Based Simulation Optimization , 2006, Proceedings of the 2006 Winter Simulation Conference.

[127]  J. P. Kelly,et al.  New advances and applications of combining simulation and optimization , 1996, Proceedings Winter Simulation Conference.

[128]  Rex K. Kincaid,et al.  Using tabu search to determine the number of kanbans and lotsizes in a generic kanban system , 1998, Ann. Oper. Res..

[129]  A. Farzanegan,et al.  Optimization of comminution circuit simulations based on genetic algorithms search method , 2009 .

[130]  N. Hu Tabu search method with random moves for globally optimal design , 1992 .

[131]  Ihsan Sabuncuoglu,et al.  Simulation optimization: A comprehensive review on theory and applications , 2004 .

[132]  A. Tamhane Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons , 1995 .

[133]  R. D. Hurrion,et al.  A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels , 1999, J. Oper. Res. Soc..

[134]  Russell R. Barton,et al.  Simulation metamodels , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[135]  B. Suman,et al.  A survey of simulated annealing as a tool for single and multiobjective optimization , 2006, J. Oper. Res. Soc..

[136]  H. Pierreval,et al.  Using evolutionary algorithms and simulation for the optimization of manufacturing systems , 1997 .

[137]  Ibrahim H. Osman,et al.  Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem , 1993, Ann. Oper. Res..

[138]  Paul Glasserman,et al.  Gradient Estimation Via Perturbation Analysis , 1990 .

[139]  Farhad Azadivar,et al.  Optimization of discrete variable stochastic systems by computer simulation , 1986 .

[140]  Thomas J. Lorenzen Minimum cost sampling plans using bayesian methods , 1985 .

[141]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[142]  J. Hsu Constrained Simultaneous Confidence Intervals for Multiple Comparisons with the Best , 1984 .

[143]  Barry L. Nelson,et al.  Simultaneous ranking, selection and multiple comparisons for simulation , 1993, WSC '93.

[144]  Peter W. Glynn Likelihood Ratio Derivative Estimators For Stochastic Systems , 1989, 1989 Winter Simulation Conference Proceedings.

[145]  M.K. Nakayama Selecting the best system in steady-state simulations using batch means , 1995, Winter Simulation Conference Proceedings, 1995..

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

[147]  Stephen M. Robinson,et al.  Sample-path optimization of convex stochastic performance functions , 1996, Math. Program..

[148]  S. Andradottir Single Run Optimization Using The Reverse-simulation Method , 1997 .

[149]  Fred Glover,et al.  Practical introduction to simulation optimization , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[150]  Sigrún Andradóttir,et al.  Chapter 20 An Overview of Simulation Optimization via Random Search , 2006, Simulation.

[151]  Claude Dennis Pegden,et al.  Decision optimization for GASP IV simulation models , 1977, WSC '77.

[152]  Chun-Hung Chen,et al.  New development of optimal computing budget allocation for discrete event simulation , 1997, WSC '97.

[153]  Russell R. Barton,et al.  Modifications of the Nelder-Mead simplex method for stochastic simulation response optimization , 1991, 1991 Winter Simulation Conference Proceedings..

[154]  Yu-Chi Ho,et al.  The problem of large search space in stochastic optimization , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[155]  Barry L. Nelson,et al.  Comparing Systems via Simulation , 2007 .

[156]  F. Hunt,et al.  Sample path optimality for a Markov optimization problem , 2005 .

[157]  L. Dai,et al.  Optimizing discrete event dynamic systems via the gradient surface method , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[158]  Michael C. Fu,et al.  Smoothed perturbation analysis derivative estimation for Markov chains , 1994, Oper. Res. Lett..

[159]  Alan Weiss,et al.  Sensitivity analysis via likelihood ratios , 1986, WSC '86.

[160]  Henri Pierreval,et al.  Distributed evolutionary algorithms for simulation optimization , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[161]  P. Shahabuddin,et al.  Likelihood Ratio Derivative Estimation for Finite-Time Performance Measures in Generalized Semi-Markov Processes , 1998 .

[162]  Gül Gürkan,et al.  Sample-path optimization in simulation , 1994, Proceedings of Winter Simulation Conference.

[163]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[164]  John M. Usher,et al.  Using evolution strategies and simulation to optimize a pull production system , 1996 .

[165]  S. Jacobson,et al.  A harmonic analysis approach to simulation sensitivity analysis , 1999 .

[166]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[167]  Volker Nissen,et al.  Combinations of simulation and Evolutionary Algorithms in management science and economics , 1994, Ann. Oper. Res..

[168]  Gerald W. Evans,et al.  Comparison of global search methods for design optimization using simulation , 1991, 1991 Winter Simulation Conference Proceedings..

[169]  Jorge Haddock,et al.  Optimization Of An Automated Manufacturing System Simulation Model Using Simulated Annealing , 1989, 1989 Winter Simulation Conference Proceedings.

[170]  Xi-Ren Cao,et al.  Perturbation analysis of discrete event dynamic systems , 1991 .

[171]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[172]  L. Dai Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems , 1995 .

[173]  Jorge Haddock,et al.  Application of a simulation optimization system for a continuous review inventory model , 1987, WSC '87.