Hachicha, Wafik and Ammeri, Ahmed and Masmoudi, Faouzi and Chachoub, Habib
(2010):
*A comprehensive literature classification of simulation optimisation methods.*
Published in: International Conference on Multiple Objective Programming and Goal Programming - MOPGP10
No. May 24- 26, 2010 - Sousse - Tunisia

Preview |
PDF
MPRA_paper_27652.pdf Download (266kB) | Preview |

## Abstract

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

Item Type: | MPRA Paper |
---|---|

Original Title: | A comprehensive literature classification of simulation optimisation methods |

Language: | English |

Keywords: | Simulation Optimization, classification methods, literature survey |

Subjects: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General Z - Other Special Topics > Z1 - Cultural Economics ; Economic Sociology ; Economic Anthropology > Z11 - Economics of the Arts and Literature |

Item ID: | 27652 |

Depositing User: | Wafik HACHICHA |

Date Deposited: | 26 Dec 2010 19:44 |

Last Modified: | 28 Sep 2019 17:50 |

References: | Almeder.C., Preusser. M. (2007). A hybrid simulation op-timization approach for supply chains. Proc. EUROSIM, pp. 9-13 Sept, Ljubljana, Slovenia Andradottir, S. (1996) Optimization of the transient and steady-state behavior of discrete event systems. Man-agement Science, 42, pp.717-737. Andradóttir, S. (1998). A review of simulation optimiza-tion techniques. In: Medeiros, D.J., Watson, E.F., Carson, J.S., Manivannan, M.S. (Eds.), Proceedings of the 1998 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 151–158. Andradottir, S. (2005). An overview of simulation opti-mization via random search. Chapter 21 in Hand-books in Operations Research and Management Sci-ence: Simulation, S.G. Henderson and B.L. Nel-son,eds., Elsevier. Andradóttir, S. (2006). An overview of simulation opti-mization with random search. In Handbooks in Op-erations Research and Management Science: Simula-tion, ed. S.G. Henderson and B.L. Nelson, Chapter 20, pp. 617-632. Elsevier. April, J., Glover, F., Kelly, J. P., & Laguna, M. (2003). Practical introduction to simulation optimization. In Proceedings of the 2003 winter simulation confer-ence, Vol. 1 pp. 71–78, New Orleans, LA. Azadivar, F. and Lee, Y. (1988) Optimization of discrete-variable stochastic systems by computer simulation. Mathematics and Computers in Simulation, 30, pp. 331-345. Azadivar, F (1992). Atutorial on simulation optimization. in Proceedings of the 1992 Winter Simulation Confer-ence. J.J. Swain, D.Goldsman, R.Ccrain and J.Rwilson, eds EEE, Piscataway, NJ, pp. 198-204. Back, T., Hammel, K. and Schwefel, H.P. (1997). Evolu-tionary computation: comments on the historical and current state. IEEE Transactions on Evolutionary Computation, 1, pp. 3-16. Barretto, M.R.P., Brito, N.M.J., Chwif, L. and Moseato, L.A. (1998). Scheduling with the LEO algorithm. Technical Report. Society of Manufacturing Engi-neers, pp.10-17. Barretto, M.R.P., Eldabi, T., Chwif, L. and Paul, J.R. (1999) Simulation optimization with the linear move and exchange move optimization algorithm, in Pro-ceedings of the 1999 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 806-811. Barton, R.R. (1992). Metamodels for simulation input–output relations. In: Swain, J.J., Goldsman, D., Crain, R.C.,Wilson, J.R. (Eds.), Proceedings of the 1992 Winter Simulation Conference. IEEE Press, Piscata-way, NJ, pp. 289–299. Barton, R.R. (1998). Simulation metamodels. In: Medeiros, D.J., Watson, E.F., Carson, J.S., Manivan-nan, M.S. (Eds.), Proceedings of the 1998 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 167–174. Barton, R.R. and J.S. Ivey. (1991). Modifications of the nelder-mead simplex method for stochastic simulation response optimization. Proceedings of the 1991 Win-ter Simulation Conference, pp.945-953. Barton, R.R. and J.S. Ivey. (1996). Nelder-mead simplex modifications for simulation optimization. Manage-ment Science 42, pp.954-973. Barton, R. R., and M. Meckesheimer. (2006). Meta-modelbased simulation optimization. In Handbooks in Operations Research and Management Science: Simulation, ed. S. G. Henderson and B. L. Nelson, Chapter 18, pp.535–574. Elsevier Bechhofer, R.E., Santner, T.J. and Goldsman, D. (1995) Design and Analysis of Experiments of Statistical Se-lection, Screening, and Multiple Comparisons, Wiley, New York, NY. Bettonvil, B. (1989). A formal description of discrete event dynamic systems including infinitesimal pertur-bation analysis. European Journal of Operational Re-search 42; pp.213-222. Biethahn, J. and Nissen, V. (1994). Combinations of simulation and evolutionary algorithms in manage-ment science and economics. Annals of Operations Research, 52, pp.183-208. Blanning W.R (1975), The construction and implementa-tion of metamodels, Simulation 24–25 (6) pp. 177–184 Bofinger, E. and G.J. Lewis. (1992). Two-stage proce-dures for multiple comparisons with a control. Ameri-can Journal of Mathematical and Management Sci-ences 12; pp.253-275. Booker, A.J., Dennis, J.E., Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W. (1999). A rigorous framework for optimization of expensive functions by surrogates. Structural Optimization 17, pp.1–13. Brady, T. and McGarvey, B. (1998). Heuristic optimiza-tion using computer simulation: a study of staffing levels in a pharmaceutical manufacturing laboratory, in Proceedings of the 1998 Winter Simulation Confer-ence. IEEE Press, Piscataway, NJ, pp. 1423-1428. Branke, J., S. E. Chick, and C. Schmidt. (2007). Selecting a selection procedure. Management Science 53; pp.1916–1932. Breiman, L. (1991). The Π method for estimating multi-variate functions from noisy data (with discussion). Technometrics 33, pp.125–160. Cassady, C.R., Bowden, R.O., Liew, L. and Pohl, E.A. (2000) Combining preventive maintenance and statis-tical process control: a preliminary investigation. IIE Transactions, 32, pp.471-478. Carson.Y and Maria. A, (1997). Simulation optimization: Methods and Applications. Proceedings of the 1997 Winter Simulation Conference Chang, H. S., M. C. Fu, J. Hu, and S. I. Marcus. (2007). Simulation-based algorithms for Markov decision processes. Springer. Chen, C.H. (1995). An effective approach to smartly al-locate computing budget for discrete event simulation. Proceedings of the IEEE Conference on Decision and Control, pp.2598-2603. Chen, C.H. (1996) A lower bound for the correct subset selection probability and its application to discrete event system simulations. IEEE Transactions on Automatic Control, 41, pp.1227-1231. Chen, H. C., C. H. Chen, L. Dai, and E. Y¨ucesan. (1997). New development of optimal computing budget allocation for discrete event simulation. In Proceedings of the 1997 Winter Simulation Confer-ence, pp.334–341. Piscataway, NJ: IEEE. Chen, C. H., Fu, M., Shi, L. (2008). Simulation and Op-timization, Tutorials in Operations Research, pp. 247-260, Informs, Hanover, MD Chong, E.K.P. and Ramadge, P.J. (1993) Optimization of queues using infinitesimal perturbation analysis. SIAM Journal on Control and Optimization, 31, pp.698-732. Dai, L. (1995). Convergence properties of ordinal com-parison in the simulation of discrete event dynamic systems. Proceedings of the IEEE Conference on De-cision and Control, pp.2604-2609. Dai, L. (2000) Perturbation analysis via coupling. IEEE Transactions on Automatic Control, 45, pp.614-628. Damerdji, H. and M.K. Nakayama. (1996). Two-stage procedures for multiple comparisons with a control in steady-state simulations. Proceedings of the 1996 Winter Simulation Conference, pp.372-375. Damerdji, H. and M.K. Nakayama. (1999). Two-stage multiple-comparison procedures for steady-state simulations. ACM Transactions on Modeling and Computer Simulations 9; pp.1-30. Deng, M. and Y.C. Ho. (1997). Iterative ordinal optimi-zation and its application. Proceedings of the IEEE Conference on Decision and Control, pp.3562-3567. Deng, M., Y.C. Ho, and J.Q. Hu. (1992). Effect of corre-lated estimation errors in ordinal optimization. Pro-ceedings of the 1992 Winter Simulation Conference, pp.466-474. Dengiz, B. and Alabas, C. (2000) Simulation optimiza-tion using tabu search, in Proceedings of the 2000 Winter Simulation Conference, IEEE Press, Piscata-way, NJ, pp. 805-810. Dolgui, A. and D. Ofitserov. (1997). A stochastic method for discrete and continuous optimization in manufac-turing systems. Journal of Intelligent Manufacturing, 8; pp.405-413. Donohue, K.L. and Spearman, M.L. (1993) Improving the design of stochastic production lines--an approach using perturbation analysis. International Journal of Production Research, 31, pp.2789-2806. Easom, E. (1990) A survey of global optimization tech-niques. M.S. thesis. University of Louisville, Louis-ville, KY. Eubank, R.L. (1988). Spline Smoothing and Nonparamet-ric Regression. Dekker, New York. Eglese, R.W. (1990) Simulated annealing: a tool for op-erational research. European Journal of Operational Research, 46, pp.271-281. Farzanegan. A, Vahidipour. S. M, (2009).Optimization of comminution circuit simulations based on genetic al-gorithms search method. Minerals Engineering 22, pp.719–726 Fleischer, M. (1995). Simulated Annealing: Past, Present, and Future, Proceedings of the 1995 Winter Simula-tion Conference, pp.155-161 Friedman, J.H. (1991). Multivariate adaptive regression splines. The Annals of Statistics 19, pp.1–141. Franke, R. (1982). Scattered data interpolation: Tests of some methods. Mathematics of Computation 38, pp.181–200. Fu, M.C. (1994) Optimization via simulation: a review. Annals of Operations Research, 53, pp. 199-247. Fu, M. C. (2006). Simulation Optimization of Traffic Light Signal Timings via Perturbation Analysis. Smith School of Business Institute for Systems Re-search. University of Maryland Fu, M. C. (2007). Are we there yet? the marriage between simulation & optimization. OR/MS Today June; pp.16-17. Fu, M. C. (2008). What you should know about simula-tion and derivatives. Naval Research Logistics. forth-coming Fu, M.C. and Hu, J.Q. (1994) Smoothed perturbation analysis derivative estimation for Markov chains. Op-erations Research Letters, 15, pp.241-251. Fu, M. C., F. W. Glover and J. April (2005) Simulation optimization: a review, new developments, and appli-cations, paper presented at Proceedings of the 2005 Winter Simulation Conference, pp.83–95, Piscataway, New Jersey, USA. Fu, M.C, Chen C.Chung, and Shi. L, (2008) Some top-ics for simulation optimization. Proceedings of the 2008 Winter Simulation Conference. Fu, M.C. and Hu, J.Q. (1999) Efficient design and sensi-tivity analysis of control charts using Monte Carlo simulation. Management Science, 45, pp.395-413. Glasserman, P. (1991). Gradient Estimation via Perturba-tion Analysis. Kluwer Academic Publishers, Boston, Massachusetts. Glover, F. (1989). Tabu Search - Part I, ORSA Journal on Computing, Vol. 1, No. 3, Summer 1989. Glover, F. (1990). Tabu Search - Part II, ORSA Journal on Computing, Vol. 2, No. 1, Winter 1990 Glover, F. and Laguna, M. (1997) Tabu Search, Kluwer, Norwell, MA. Glover, F., J. P. Kelly, and M. Laguna. (1996). New Ad-vances and Applications of Combining Simulation and Optimization, Proceedings of the 1996 Winter Simulation Conference, pp.144-152. Glynn, P.W. (1987). Likelihood ratio gradient estimation: an overview. Proceedings of the 1987 Winter Simula-tion Conference, pp.366-375. Glynn, P. W. (1989a). Likelihood Ratio Derivative Esti-mators for Stochastic Systems, Proceedings of the 1989 Winter Simulation Conference, pp. 374-380. Glynn, P. W. (1989b). Optimization of Stochastic Sys-tems Via Simulation, Proceedings of the 1989 Winter Simulation Conference, pp.90-105. Goldsman, D. and B.L. Nelson. (1990). Batch-size effects on simulation optimization using multiple compari-sons. Proceedings of the 1990 Winter Simulation Conference, pp.288-293. Goldsman, D. and B.L. Nelson. (1998). Comparing sys-tems via simulation. In Handbook on Simulation, ed. J. Banks, John Wiley & Sons, Inc., New York. Goldsman, D. and B.L. Nelson. (1998b). Statistical screening, selection and multiple comparison proce-dures in computer simulation. Proceedings of the 1998 Winter Simulation Conference, pp.159-166. Gurkan, G., A.Y. Ozge, and S.M. Robinson. (1994). Sample-path optimization in simulation. Proceedings of the 1994 Winter Simulation Conference, pp.247-254. Hachicha, W., Ammeri, A., Masmoudi, F., Chabchoub, H. (2010). A multi-product lot size in make-to-order supply chain using discrete event simulation and re-sponse surface methodology. International Journal of Services, Economics and Management, 2(3-4), pp. 246-266 Haddock, J. and G. Bengu. (1987). Application of a simu-lation optimization system for a continuous review inventory system. Proceedings of the 1987 Winter Simulation Conference, pp.382-390 Haddock, J. and J. Mittenhall. (1992). Simulation optimi-zation using simulated annealing. Computers and In-dustrial Engineering 22; pp.387-395. Hall, J.D. and R.O. Bowden. (1997). Simulation optimi-zation by direct search: a comparative study. Proceed-ings of the 6th International Industrial Engineering Research Conference, pp.298-303. Hall, J., Bowden, R. and Usher, J. (1996) Using evolution strategies and simulation to optimize a pool produc-tion system. Journal of Materials Processing Tech-nology, 61, pp.47-52. Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms (2nd ed.). New York: John Wiley. Hazra, M.M., Morrice, D.J. and Park, S.K. (1997) A simulation clock-based solution to the frequency-domain experiment indexing problem. IIE Transac-tions, 29, pp.769-782. Hedar, A. R., & Fukushima, M. (2006). Tabu search di-rected by direct search methods for nonlinear global optimization. European Journal of Operational Re-search, 170(3), pp.329–349 Heidergott, B. (1995) Sensitivity analysis of a manufac-turing workstation using perturbation analysis tech-niques. International Journal of Production Research, 33, pp.611-622. Healy K. J. and L. W. Schruben. (1991). Retrospective simulation response optimization. In Proceedings of the 1991 Winter Simulation Conference, ed. B. L. Nelson, W. D. Kelton, and G. M. Clark, pp.901–906. Institute of Electrical and Electronics Engineers, Pis-cataway, New Jersey. Healy K. J. and Y. Xu. (1994). Simulation based retro-spective approaches to stochastic system optimiza-tion. Preprint Ho, Y.C. (1994). Overview of ordinal optimization. Pro-ceedings of the IEEE Conference on Decision and-Control, pp.1975-1977. Ho, Y.C. and M. Deng. (1994). The problem of large search space in stochastic optimization. Proceedings of the IEEE Conference on Decision and Control, pp. 1470-1475. Ho, Y.C. and M.E. Larson. (1995). Ordinal optimization approach to rare event probability problems. Discrete Event Dynamic Systems: Theory and Applications 5; pp.281-301. Ho, Y.C., Shi, L., Dai, L. and Gong, W.B. (1992) Opti-mizing discrete event dynamic systems via the gradi-ent surface method. Discrete Event Dynamic Systems, 2, pp.99-120. Ho, Y.C., R. Sreenivas, and P. Vakili. (1992). Ordinal optimization of discrete event dynamic systems. Dis-crete Event Dynamical Systems 2, pp.61-88. Ho, Y.C. and Cao, R. (1991) Perturbation Analysis of Discrete Event Dynamic Systems, Kluwer, Norwell, MA. Hu, N.F. (1992) Tabu search method with random moves for globally optimal-design. International Journal for Numerical Methods in Engineering, 35, pp.1055-1070. Hsu, J.C. (1984). Constrained simultaneous confidence intervals for multiple comparisons with the best. An-nals of Statistics 12; pp.1136-1144. Hsu, J.C. and B.L. Nelson. (1988). Optimization over a finite number of system designs with one-stage sam-pling and multiple comparisons with the best. Pro-ceedings of the 1988 Winter Simulation Conference, pp.451-457. Humphrey, D.G. and J.R. Wilson. (1998). A revised sim-plex search procedure for stochastic simulation re-sponse-surface optimization. Proceedings of the 1998 Winter Simulation Conference, pp.751-759. Hurrion. R.D, S. Birgil, (1999) A comparison of factorial and random experimental design methods for the de-velopment of regression and neural network simula-tion metamodels, Journal of the Operational Re-search Society 50 pp.1018–1033. Hunt, F. Y. (2005). Sample path optimality for a Markov optimization problem. Stochastic Processes and Their Applications, 115(6), pp.769–779. Hyden, P. and L.W. Schruben. (1999). Designing simul-taneous simulation experiments. Proceedings of the 1999 Winter Simulation Conference, pp.389-394 Jack P.C. Kleijnen (2008). Response surface methodol-ogy for constrained simulation optimization: An overview. Simulation Modelling Practice and Theory 16, pp. 50–64 Jacobson, S.H. and L.W. Schruben. (1999). A harmonic analysis approach to simulation sensitivity analysis. IIE Transactions 31, pp. 231-243. Jaluria, Y ; (2009). Simulation-based optimization of thermal systems. Applied Thermal Engineering 29, pp.1346–1355 Johnson, D.S., Aragon, C.R., McGeoch, K.A. and Schevon, C. (1989) Optimization by simulated an-nealing: an experimental evaluation: part 1, graph par-titioning. Operations Research, 37, pp.865-893. Kabirian, A., Olafsson, S., (2007). Allocation of simula-tion runs for simulation optimization. In: Proceedings of the 2007 Winter Simulation Conference, pp 363-371. Kampf, M. & Kochel, P. (2006). Simulation-based se-quencing and lot size optimisation for a production-and-inventory system with multiple items. Interna-tional Journal of Production Economics, 104, pp.191-200 Kim.S and Nelson.B (2006). Selecting the best system. Handbook in OR & MS, Vol. 13 Kim, S. (2006). Gradient-based simulation optimization. In Proceedings of the 2006 winter simulation confer-ence ; pp. 159–167 Monterey, CA. Kleijnen, J.P.C. (1987). Statistical Tools for Simulation Practitioners. Dekker, New York. Kleijnen, J.P.C. (1995). Sensitivity analysis and optimi-zation simulation: design of experiments and case studies, In Proceedings of the 1995 winter simulation conference, C. Alexopoulos, K.Kang, W. R. Lilegdon and D.Goldsman, eds IEEE, Piscataway, N,J pp. 133–140. Kleijnen, J.P.C. (2005). An overview of the design and analysis of simulation experiments for sensitivity analysis. European Journal of Operational Research 164, pp.287–300. Kleijnen, J. (2008). Design and analysis of simulation experiments. New York: Springer. Kleywegt, A., A. Shapiro, and T. Homem-de-Mello. (2001). the sample average approximation method for stochastic discrete optimization. SIAM Journal on Op-timization 12 ; pp.479–502. Koenig, L.W. and A.M. Law. (1985). A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation. Communications in Statistics B14:719 Koulamas, C., Antony, S.R. and Jaen, R. (1994) A survey of simulated annealing applications to operations re-search problems. International Journal of Manage-ment Sciences, 22, pp. 41-56. Kochel, P.; Kunze, S.; Nielander, U., (2003). Optimal control of a distributed service system with moving resources: Application to the fleet sizing and alloca-tion problem. Internat. Journal of Production Eco-nomics, v.81-82, pp.443-459 Kochel, P. & Nielander, U. (2002). Kanban optimization by simulation and evolution. Production Planning & Control, 13, pp.725-734 Kochel,P. & Nielander, U.(2005). Simulation-based op-timisation of Multi-echelon inventory systems. Inter-national Journal of Production Economics, 93-94, pp.505-513. Lau, T.W.E. and Ho, Y.C. (1997) Universal alignment probabilities and subset selection for ordinal optimi-zation. Journal of Optimization Theory Applications, 93, pp. 455-489. Law, A.M., Kelton, W.D. (2000). Simulation Modeling and Analysis, 3rd edition. McGraw Hill, New York. Lee, L.H, T.W.E. Lau., and Y.C. Ho. (1999). Explanation of goal softening in ordinal optimization. IEEE Transactions on Automatic Control (44); pp.94-98. Lee, Y.H., K.J. Park, and Y.B. Kim. (1997). Single run optimization using the reverse-simulation method. Proceedings of the 1997 Winter Simulation Confer-ence, pp.187-193. Liepins, G.E. and Hilliard, M.R. (1989) Genetic algo-rithms: foundations and applications. Annals of Op-erations Research, 21, pp.31-58. Lorenzen, T.J. (1985) Minimum cost sampling plans us-ing Bayesian methods. Naval Research Logistics, 32, pp.57-69. Lutz, C.M., Davis, K.R. and Sun, M.H. (1998) Determin-ing buffer location and size in production lines using tabu search. European Journal of Operational Re-search, 106, pp.301-316. Mahmoud H. Alrefaei , Ali H. Diabat (2009) A simulated annealing technique for multi-objective simulation optimization. Applied Mathematics and Computation 215, pp.3029–3035 Manz, E.M., Haddock, J. and Mittenthal, J. (1989) Opti-mization of an automated manufacturing system simulation model using simulated annealing, in Pro-ceedings of the 1989 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 390-395. Maria, A. (1995). Genetic Algorithm for Multimodal Continuous Optimization Problems, PhD Disserta-tion, University of Oklahoma, Norman. Martin, A.D., Chang, T.M., Yih, Y. and Kincaid, R.K. (1998) Using tabu search to determine the number of kanbans and lotsizes in a generic kanban system. An-nals of Operations Research, 78, pp.201-217. Matejcik, F.J. and B.L. Nelson. (1993). Simultaneous ranking, selection and multiple comparisons for simu-lation. Proceedings of the 1993 Winter Simulation Conference, pp.386-392. Matejcik, F.J. and B.L. Nelson. (1995). Two-stage multi-ple comparisons with the best for computer simula-tion. Operations Research 43, pp.633-640. Másson, E.,Wang, Y.-J. (1990). Introduction to computa-tion and learning in artificial neural networks. Euro-pean Journal of Operational Research 47, pp.1–28. Meckesheimer, M., Booker, A.J., Barton, R.R., Simpson, T.W. (2002). Computationally inexpensive meta-model assessment strategies. AIAA Journal 40, pp. 2053–2060. Mitchell, T.J., Morris, M.D. (1992). The spatial correla-tion function approach to response surface estimation. In: Swain, J.J., Goldsman, D., Crain, R.C., Wilson, J.R. (Eds.), Proceedings of the 1992 Winter Simula-tion Conference. IEEE Press, Piscataway, NJ, pp. 565–571. Morrice, D. J., and L. W. Schruben. (1989). Simulation Sensitivity Analysis Using Frequency Domain Ex-periments, Proceedings of the 1989 Winter Simulation Conference, pp.367-373. Morrice, D.J., J. Butler, and P.W. Mullarkey. (1998). An approach to ranking and selection for multiple per-formance measures. Proceedings of the 1998 Winter Simulation Conference, pp.719-725. Morrice, D.J., J. Butler, P.W. Mullarkey, and S. Gavireni. (1999). Sensitivity analysis in ranking and selection for multiple performance measures. Proceedings of the 1999 Winter Simulation Conference, pp.618-624. Muhlenbein, H. (1997). Genetic algorithms. In Local Search in Combinatorial Optimization, eds. E. Aarts and J.K. Lenstra, pp.137-172. Myers, R.H., Montgomery, D.C. (2002). Response Sur-face Methodology: Process and Product Optimization Using Designed Experiments, 2nd edition. Wiley, New York. Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., & Kowalski, S. M. (2004). Response surface methodology: A retrospective and literature survey. Journal of Quality Technology, 36(1), pp.53–77 Nakayama, M.K. (1995). Selecting the best system in steady-state simulations using batch means. Proceed-ings of the 1995 Winter Simulation Conference, pp.362-366. Nakayama, M.K. (1996) Multiple comparisons with the best in steady-state simulations. Proceedings of the Second International Workshop on Mathematical Methods in Stochastic Simulation and Experimental Design, pp.230-235. Nakayama, M.K. (1997). Multiple-comparison proce-dures for steady-state simulations. Annals of Statistics 25:2433-2450. Nakayama, M.K. and Shahabuddin, P. (1998) Likelihood ratio derivative estimation for finite-time performance measures in generalized semi-Markov processes. Management Science, 44, pp.1426-1441. Nakayama, M.K., Goyal, A. and Glynn, P.W. (1994) Likelihood ratio sensitivity analysis for Markovian models of highly dependable systems. Operations Re-search, 42, pp.137-157. Nakayama, M.K. (2000). Multiple comparisons with the best using common random numbers in steady-state simulations. Journal of Statistical Planning and Infer-ence 85; pp.37-48. Nelson, B.L., Swann, J., Goldsman, D. and Song, W. (2001) Simple procedures for selecting the best simu-lated system when the number of alternatives is large. Operations Research, 49, pp.950-963. Netlab(2005).HowtouseNetlab.Available at http://homepages.cae.wisc.edu/~ece539/software/ net-lab/intro.htm. Nocedal, J., & Wright, S. J. (2006). Numerical optimiza-tion (2nd ed.). New York: Springer-Verlag Ólafsson, S and Kim, J. (2002). Simulation optimiza-tion: towards a framework for black-box simulation optimization, Proceedings of the 2002 Winter Simula-tion Conference E. Yücesan, C.-H. Chen, J. L. Snow-don, and J. M. Charnes, eds. Osman, I. H. (1993). Metastrategy Simulated Annealing and Tabu Search Algorithms for the Vehicle Routing Problem, Annals of Operations Research, 41, pp.421-451 Paul. Ray. J and Chanev. Tomas.S. (1998). Simulation optimisation using a genetic algorithm. Simulation Practice and Theory 6, pp.601–611. Pegden, C.D. and Gately, M.P. (1977) Decision optimiza-tion for GASP 4 simulation models, in Proceedings of the 1977 Winter Simulation Conference. IEEE Press, Piscataway, NJ, pp. 125-133. Pflug, G. C. (1996). Optimization of stochastic models. Kluwer Academic. Pierreval, H. and Paris, J. (2000) Distributed evolutionary algorithms for simulation optimization. IEEE Trans-actions on Systems, Man and Cybernetics, Part A: systems and Humans, 30, pp.15-24. Pierreval, H. and Tautou, L. (1997) Using evolutionary algorithms and simulation for the optimization of manufacturing systems. IIE Transactions, 29, pp.181-189. Piera, M.A., Guasch, A., and Riera, D., (2004). Optimiza-tion of logistic and manufacturing systems through simulation- a colored Petri net-based methodology, Simulation, 80(3), 121-129. Plambeck, E. L., B.-R. Fu, S. M. Robinson, and R. Suri. (1996). Sample-path optimization of convex stochas-tic performance functions. Mathematical Program-ming 75; pp.137–176. Reiman, M. I. and A. Weiss. (1986). Sensitivity Analysis Via Likelihood Ratios, Proceedings of the 1986 Win-ter Simulation Conference, 285-289. Robinson, S.M. (1996). Analysis of sample-path optimi-zation. Mathematics of Operations Research 21; pp.513- 528. Rosen. S.L, Harmonosky.C.M and Traband.M.T. (2007). A simulation optimization method that considers un-certainty and multiple performance measures. Euro-pean Journal of Operational Research 181, 315–330 Rubinstein, R.Y. (1991). How to optimize discrete-event systems from a single sample path by the score func-tion method. Annals of Operations Research 27; pp.175-212. Rubinstein, R.Y. and A. Shapiro. (1993). Discrete Event Systems: Sensitivity Analysis and Stochastic Ap-proximation using the Score Function Method, John Wiley & Sons, Inc., Chichester Santner, T.J., Williams, B.J., Notz, W.I. (2003). The De-sign and Analysis of Computer Experiments. Spinger-Verlag, New York. Schruben, L.W. (1997). Simulation optimization using simultaneous replications and event time dilation. Proceedings of the 1997 Winter Simulation Confer-ence, pp.177-180. Schruben, L.W. and Cogliano, V.J. (1981) Simulation sensitivity analysis: a frequency domain approach, in Proceedings of the 1981 Winter Simulation Confer-ence, IEEE Press, Piscataway, NJ, pp. 455-459. Schwefel, H. P. (1995). Evolution and Optimum Seeking, John Wiley. Shapiro, A., and Y. Wardi. (1996). Convergence analysis of gradient descent stochastic algorithms. Journal on Optimization Theory and Application 91; pp.439–454. Shi, L. and S. Ólafsson. (1997). An integrated framework for deterministic and stochastic optimization .Proceedings of the 1997 Winter Simulation Confer-ence, pp.358-365. Shi, L., C.H. Chen, and E. Yücesan. (1999). Simultane-ous simulation experiments and nested partition for discrete resource allocation in supply chain manage-ment. Proceedings of the 1999 Winter Simulation Conference, pp.395-401. Shin, M., Sargent, R.G., Goel, A.L. (2002). Gaussian radial basis functions for simulation metamodeling. In: Yücesan, E., Chen, C.-H., Snowdon, J.L., Charnes, J.M. (Eds.), Proceedings of the 2002 Winter Simula-tion Conference. IEEE Press, Piscataway, NJ, pp. 483–488 Simpson, T.W., Mauery, T.M., Korte, J.J., Mistree, F. (1998). Comparison of response surface and kriging-models for multidisciplinary design optimization. In: 7th Symposium on Multidisciplinary Analysis and Op-timization. AIAA-98-4755. AIAA, St. Louis, MO, pp. 381–391. Stuckman, B., G. Evans, and M. Mollaghasemi. (1991). Comparison of Global Search Methods for Design Optimization Using Simulation, Proceedings of the 1991 Winter Simulation Conference, pp.937-944. Suman, B., & Kumar, P. (2006). A survey of simulated annealing as a tool for single and multiobjective op-timization. Journal of the Operational Research Soci-ety, 57(10), pp.1143–1160. Swisher, J.R., P.D. Hyden, S.H. Jacobson, and L.W. Schruben. (2004). A survey of of recent advances.in discrete in put parameter discrete-event simulation optimization. IIE Transactions, 36, pp.591–600. Swisher, J.R. and P.D. Hyden (2000). A survey of simu-lation optimization techniques and procedures. Pro-ceedings of the 2000 Winter Simulation Conference. Tekin, E., & Sabuncuoglu, I. (2004). Simulation optimi-zation: A comprehensive review on theory and appli-cations. IIE Transactions, 36(11), pp.1067–1081. Tompkins, G. and F. Azadivar. (1995). Genetic algo-rithms in optimizing simulated systems. Proceedings of the 1995 Winter Simulation Conference, pp.757-762. Tomick, J., Arnold, S., Barton, R.R. (1995). Sample size selection for improved Nelder–Mead performance. In: Alexopolous, C., Kang, K., Lilegdon,W.R., Golds-man, D. (Eds.), Proceedings of the 1995 Winter Simu-lation Conference. IEEE Press, Piscataway, NJ, pp. 341–345 Tukey, J.W. (1953). The problem of multiple compari-sons. Unpublished manuscript. Tu, C.-H., Barton, R.R. (1997). Production yield estima-tion by the metamodel method with a boundaryfo-cused experiment design. In: Proceedings of DETC’97, 1997 ASME Design Engineering Technical Conference. DETC97/DTM3870. ASME, Fairfield, NJ. Van Laarhoven, P.J.M. and Aarts, E.H.L. (1987) Simu-lated Annealing: Theory and Applications, Reidel, Dordrecht, The Netherlands. Xie, X. (1997). Dynamics and convergence rate of ordi-nal comparison of stochastic discrete-event systems. IEEE Transactions on Automatic Control 42:586-590. Yoo.T; Cho.H and Yücesan.E (2010) Hybrid algorithm for discrete event simulation based supply chain op-timization. Expert Systems with Applications 37, pp.2354–2361 Yesilyurt, S., Patera, A.T. (1995). Surrogates for numeri-cal simulations; optimization of eddy-promoter heat exchangers. Computer Methods in Applied Mechanics and Engineering 121, pp. 231–257. Yuan.F,C; (2009). Simulation–optimization mechanism for expansion strategy using real option theory. Ex-pert Systems with Applications 36, pp.829–837 Yuan, M. and B.L. Nelson. (1993). Multiple comparisons with the best for steady-state simulation. ACM Trans-actions on Modeling and Computer Simulation 3; pp.66-79. Yucesan, E. and Jacobson, S.H. (1996) Computational issues for accessibility in discrete event simulation. ACM Transactions on Modeling and Computer Simu-lation, 6, pp.53-75. Zeng, J. and J. Wu. (1993). DEDS (discrete event dy-namic systems) simulation-optimization algorithm us-ing simulated-annealing combined with perturbation analysis. Zidonghua Xuebao Acta Automatica Sinica, 19; pp.728-731. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/27652 |