Feature Article: Optimization for simulation: Theory vs. Practice

References

  • Andradóttir S. A global search method for discrete stochastic optimization. SIAM Journal on Optimization (1996) 6:513–530CrossrefGoogle Scholar
  • Andradóttir S., Banks J. Simulation optimization. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (1998) (John Wiley & Sons, New York) . Chapter 9 inCrossrefGoogle Scholar
  • Banks J.Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (1998) (John Wiley & Sons, New York) CrossrefGoogle Scholar
  • Banks J., Carson J. S., Nelson B. L., Nicol D. M.Discrete Event Systems Simulation (2000) 3rd ed.(Prentice Hall, Englewood Cliffs, NJ) Google Scholar
  • Barton R. Simulation metamodels. Proceedings of the Winter Simulation Conference (1998) 167–174CrossrefGoogle Scholar
  • Bechhofer R. E., Santner T. J., Goldsman D. M.Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons (1995) (John Wiley & Sons, New York) Google Scholar
  • Bitron J. OptimizingAutoMod Models with Auto-Stat. AutoFlash (2000) (Monthly Newsletter, AutoSimulations, Inc., Bountiful, UT) . FebruaryGoogle Scholar
  • Boesel J.Search and Selection for Large-Scale Stochastic Optimization (1999) . Ph.D. dissertation Department of Industrial Engi-neeringand Management Sciences, Northwestern University, Evanston, ILGoogle Scholar
  • Boesel J., Nelson B. L., Ishii N. A framework for simulation-optimization software. IIE Transactions (2001) . forthcomingGoogle Scholar
  • Boesel J., Nelson B. L., Kim S.-H. Usingrankingand selection to ‘clean up’ after simulation optimization. submitted for publication. Operations Research (2001) Google Scholar
  • Bonabeau E., Dorigo M., Theraulaz T.From Natural to Artificial Swarm Intelligence (1999) (Oxford University Press, New York) CrossrefGoogle Scholar
  • Bucklew J. A.Large Deviations Techniques in Decision, Simulation, and Estimation (1990) (John Wiley & Sons, New York) Google Scholar
  • Chen H. C., Chen C. H., Yücesan E. Computingefforts allocation for ordinal optimization and discrete event simulation. IEEE Transactions on Automatic Control (2000) 45:960–964CrossrefGoogle Scholar
  • Chen H. C., Lin J., Yücesan E., Chick S. E. Simulation budget allocation for further enhancingthe efficiency of ordinal optimization. Journal of Discrete Event Dynamic Systems: Theory and Applications (2000) 10:251–270CrossrefGoogle Scholar
  • Corne D., Dorigo M., Glover F.New Ideas in Optimisation (1999) (McGraw-Hill, New York) Google Scholar
  • Dai L. Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems. Journal of Optimization Theory and Applications (1996) 91:363–388CrossrefGoogle Scholar
  • Dai L., Chen C. Rate of convergence for ordinal comparison of dependent simulations in discrete event dynamic systems. Journal of Optimization Theory and Applications (1997) 94:29–54CrossrefGoogle Scholar
  • Dembo A., Zeitouni O.Large Deviations Techniques and Applications (1998) 2nd ed.(Springer-Verlag, Berlin, Germany) CrossrefGoogle Scholar
  • Dorigo M., Di Caro G., Corne D., Dorigo M., Glover F. The ant colony optimization meta-heuristic. New Ideas in Optimization (1999) (McGraw-Hill, New York) 11–32CrossrefGoogle Scholar
  • Fu M. C. Optimization via simulation: A review. Annals of Operations Research (1994) 53:199–248CrossrefGoogle Scholar
  • Fu M. C., Andradóttir S., Carson J. S., Glover F., Harrell C. R., Ho Y. C., Kelly J. P., Robinson S. M. Integrating optimization and simulation: research and practice. Proceedings of the Winter Simulation Conference (2000) 610–616 http://www.informs-cs.org/wsc00papers/082.PDFCrossrefGoogle Scholar
  • Fu M. C., Hill S. D. Optimization of discrete event systems via simultaneous perturbation stochastic approximation. IIE Transactions (1997) 29:233–243CrossrefGoogle Scholar
  • Fu M. C., Hu J. Q. Sensitivity analysis for Monte Carlo simulation of option pricing. (1995) 9:417–446Probability in the Engineering and Information SciencesGoogle Scholar
  • Fu M. C., Hu J. Q.Conditional Monte Carlo: Gradient Estimation and Optimization Applications (1997) (Kluwer Academic, Boston, MA) CrossrefGoogle Scholar
  • Gass S. I., Harris C. M.Encyclopedia of Operations Research and Management Science (2000) 2nd ed.(Kluwer Academic, Boston, MA) Google Scholar
  • Gerencsér L. Optimization over discrete sets via SPSA. Proceedings of the IEEE Conference on Decision and Control (1999) 1791–1795CrossrefGoogle Scholar
  • Glasserman P.Gradient Estimation Via Perturbation Analysis (1991) (Kluwer Academic, Boston, MA) Google Scholar
  • Goldsman D., Nelson B. L., Banks J. Comparingsystems via simulation in. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (1998) (John Wiley & Sons, New York) 273–306Chapter 8CrossrefGoogle Scholar
  • Glover F., Kelly J. P., Laguna M. New advances for wedding optimization and simulation. Proceedings of the Winter Simulation Conference (1999) 255–260CrossrefGoogle Scholar
  • Goldsman D., Nelson B. L., Opicka T., Pritsker A. A. B. A rankingand selection project: Experiences from a university-industry collaboration. Proceedings of the Winter Simulation Conference (1999) 83–92Google Scholar
  • Gürkan G., Y. Özge A., Robinson S. M. Sample-path solution of stochastic variational inequalities. Mathematical Programming (1999) 84:313–333CrossrefGoogle Scholar
  • Hochberg Y., Tamhane A. C.Multiple Comparison Procedures (1987) (John Wiley & Sons, New York) CrossrefGoogle Scholar
  • Ho Y. C., Cao X. R.Discrete Event Dynamics Systems and Perturbation Analysis (1991) (Kluwer Academic, Boston, MA) CrossrefGoogle Scholar
  • Ho Y. C., Cassandras C. G., Chen C. H., Dai L. Y. Ordinal optimization and simulation. Journal of Operations Research Society (2000) 51:490–500CrossrefGoogle Scholar
  • Ho Y. C., Sreenivas R., Vakili P. Ordinal optimization of DEDS. Discrete Event Dynamic Systems: Theory and Applications (1992) 2:61–88CrossrefGoogle Scholar
  • Jacobson S. H., Schruben L. W. A review of techniques for simulation optimization. Operations Research Letters (1989) 8:1–9CrossrefGoogle Scholar
  • Kapuscinski R., Tayur S. R., Tayur S. R., Ganeshan R., Magazine M. J. ptimal policies and simulation based optimization for capacitated production inventory systems. Quantitative Models for Supply Chain Management (1999) (Kluwer Academic, Boston, MA) . Chapter 2 inCrossrefGoogle Scholar
  • Kleijnen J. P. C., Banks J. Experimental design for sensitivity analysis, optimization, and validation of simulation models. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (1998) (John Wiley & Sons, New York) . Chapter 6 inCrossrefGoogle Scholar
  • Law A. M., Kelton W. D. Simulation Modeling and Analysis. (2000) 3rd ed.(McGraw-Hill, New York) Google Scholar
  • Nelson B. L., Swann J., Goldsman D., Song W. Simple procedures for selectingthe best simulated system when the number of alternatives is large. Operations Research (2001) 49:950–963LinkGoogle Scholar
  • Niederreiter H.Random Number Generation and Quasi-Monte Carlo Methods (1992) (SIAM, Philadelphia, PA) CrossrefGoogle Scholar
  • Niederreiter H., Spanier J.Monte Carlo and Quasi-Monte Carlo Methods (2000) (Springer, New York) Google Scholar
  • Pflug G. C.Optimization of Stochastic Models (1996) (Kluwer Academic, Boston, MA) CrossrefGoogle Scholar
  • Robinson S. M. Analysis of sample-path optimization. Mathematics of Operations Research (1996) 21:513–528LinkGoogle Scholar
  • Rubinstein R. Y., Shapiro A.Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method (1993) (John Wiley & Sons, New York) Google Scholar
  • Sanchez S. M. Robust design: seeking the best of all possible worlds. Proceedings of the Winter Simulation Conference (2000) 69–76 http://www.informs-cs.org/wsc00papers/013.PDFCrossrefGoogle Scholar
  • Shi L., Olafsson S. Nested partitioned method for global optimization. Operations Research (2000) 48:390–407LinkGoogle Scholar
  • Shwartz A., Weiss A.Large Deviations for Performance Analysis: Queue, Communications, and Computing (1998) 2nd ed.(Springer-Verlag, Berlin, Germany) Google Scholar
  • Spall J. C. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control (1992) 37:332–341CrossrefGoogle Scholar
  • Swisher J. R., Hyden P. D., Jacobson S. H., Schruben L. W. Discrete-event simulation optimization: a survey of recent advances. IIE Transactions (2001) . SubmittedGoogle Scholar
  • Varadhan S.Large Deviations and Applications (1984) (SIAM, Philadelphia, PA) CrossrefGoogle Scholar
  • Wolff R. W.Stochastic Modeling and the Theory of Queues (1989) (Prentice Hall, Englewood Cliffs, NJ) Google Scholar
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