Article

Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problems

Authors:

Suyan TengAuthors Info & Claims

WSC '06: Proceedings of the 38th conference on Winter simulation

Pages 294 - 303

Published: 03 December 2006 Publication History

Abstract

In this paper, we consider a multi-objective simulation optimization problem with three features: huge solution space, high uncertainty in performance measures, and multi-objective problem which requires a set of nondominated solutions. Our main purpose is to study how to integrate statistical selection with search mechanism to address the above difficulties, and to present a general solution framework for solving such problems. Here due to the multi-objective nature, statistical selection is done by the multi-objective computing budget allocation (MOCBA) procedure. For illustration, MOCBA is integrated with two meta-heuristics: multi-objective evolutionary algorithm (MOEA) and nested partitions (NP) to identify the nondominated solutions for two inventory management case study problems. Results show that, the integrated solution framework has improved both search efficiency and simulation efficiency. Moreover, it is capable of identifying a set of non-dominated solutions with high confidence.

References

[1]

Ahmed, M. A., T. M. Alkhamis. 2002. Simulation-based optimization using simulated annealing with ranking and selection. Computers & Operations Research 29, 387--402.

[2]

Alkhamis, T. M., M. A. Ahmed. 2004. Simulation-based optimization using simulated annealing with confidence interval. Proceedings of the 2004 Winter Simulation Conference, 514--519.

Digital Library

[3]

Alrefaei, M. H., S. Andradóttir. 1997. Accelerating the convergence of the stochastic ruler method for discrete stochastic optimization. Proceedings of the 1997 Winter Simulation Conference, 352--357.

Digital Library

[4]

Andradóttir, S. 1999. Accelerating the convergence of random search methods for discrete stochastic optimization. ACM Transactions on Modeling and Computer Simulation 9 (4), 349--380.

Digital Library

[5]

Baesler, F. F., J. A. Sepúlveda. 2000. Multi-response simulation optimization using stochastic genetic search within a goal programming framework. Proceedings of the 2000 Winter Simulation Conference, 788--794.

Digital Library

[6]

Boesel, J., B. L. Nelson, and N. Ishii. 2003. A framework for simulation-optimization software. IIE Transactions 35, 221--229.

[7]

Butler, J., D. J. Morrice, and P. W. Mullarkey. 2001. A multiple attribute utility theory approach to ranking and selection. Management Science 47(6): 800--816.

Digital Library

[8]

Chen, C. H., J. W. Lin, E. Yücesan, and S. E. Chick. 2000. Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems: Theory and Applications 10: 251--270.

Digital Library

[9]

Chew, E. P., L. H. Lee, and S. D. Liu. 2005. Dynamic rationing policies for inventory systems with multiple demand classes. Working paper, Department of Industrial & Systems Engineering, National University of Singapore.

[10]

Chew E. P., L. H. Lee, S. Y. Teng, and C. H. Koh. 2006. Multi-objective simulation optimization for differentiated service inventory problems. Submitted to Journal of the Operational Research Society.

[11]

Chick, S. E. and K. Inoue. 2001. New two-stage and sequential procedures for selecting the best simulated system. Operations Research 49 (5): 732--743.

Digital Library

[12]

Fonseca, C. M. and P. J. Fleming. 1995. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1), 1--16.

Digital Library

[13]

Gong, W. B., Y. C. Ho, and W. G. Zhai. 1999. Stochastic comparison algorithm for discrete optimization with estimation. SIAM Journal on control and optimization 10, 384--404.

Digital Library

[14]

Hanne, T. and S. Nickel. 2005. A multiobjective evolutionary algorithm for scheduling and inspection planning in software development projects. European Journal of Operational Research 167, 663--678.

[15]

Hedlund, H. E., and M. Mollaghasemi. 2001. A genetic algorithm and an indifference-zone ranking and selection framework for simulation optimization. Proceedings of the 2001 Winter Simulation Conference, 417--421.

Digital Library

[16]

Lee, L. H., E. P. Chew, S. Y. Teng, and D. Goldsman. 2004. Optimal computing budget allocation for multiobjective simulation models. In Proceedings of the 2004 Winter Simulation Conference (ed. R. G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters), 586--594.

Digital Library

[17]

Lee, L. H., E. P. Chew, S. Y. Teng, and D. Goldsman. 2005. Finding the non-dominated Pareto set for multiobjective simulation models. Submitted to IIE Transactions.

[18]

Lee, L. H., E. P. Chew, S. Y. Teng, and Y. K. Chen 2006. Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem. Submitted to European Journal of Operational Research.

[19]

Nelson, B. L., J. Swann, D. Goldsman, and W. M. Song. 2001. Simple procedures for selecting the best simulated system when the number of alternatives is large. Operations Research 49: 950--963.

Digital Library

[20]

Ólafsson, S. 1999. Iterative ranking-and-selection for large-scale optimization. Proceedings of the 1999 Winter Simulation Conference, 479--485.

Digital Library

[21]

Shi, L. and S. Ólafsson. 2000. Nested partitions method for global optimization. Operations Research 48 (3), 390--407.

Digital Library

[22]

Swisher, J. R. and S. H. Jacobson. 2002. Evaluating the design of a family practice healthcare clinic using discrete-event simulation. Health Care Management Science 5 (2): 75--88.

[23]

Swisher, J. R., S. H. Jacobson, and E. Yücesan. 2003. Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey. ACM Transactions on Modeling and Computer Simulation 13 (2): 134--154.

Digital Library

[24]

Yan D., and H. Mukai. 1992. Stochastic discrete optimization. SIAM Journal on control and optimization 30, 594--612.

Digital Library

Cited By

Lee LChew ELi H(2011)Multi-objective COMPASS for discrete optimization via simulationProceedings of the Winter Simulation Conference10.5555/2431518.2432004(4070-4079)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2432004
Chen ELee L(2009)A multi-objective selection procedure of determining a Pareto setComputers and Operations Research10.1016/j.cor.2008.06.00336:6(1872-1879)Online publication date: 1-Jun-2009
https://dl.acm.org/doi/10.1016/j.cor.2008.06.003
Szechtman RYücesan EJefferson TFowler JIngalls R(2008)A new perspective on feasibility determinationProceedings of the 40th Conference on Winter Simulation10.5555/1516744.1516803(273-280)Online publication date: 7-Dec-2008
https://dl.acm.org/doi/10.5555/1516744.1516803

Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problems

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Published In

cover image ACM Conferences

WSC '06: Proceedings of the 38th conference on Winter simulation

December 2006

2429 pages

ISBN:1424405017

Editors:
L. Felipe Perrone
Bucknell University
,
Barry G. Lawson
University of Richmond
,
Jason Liu
Colorado School of Mines
,
Frederick P. Wieland
Sensis Corporation
,
General Chair:
David Nicol
University of Illinois, Urbana-Champaign
,
Program Chair:
Richard Fujimoto
College of Computing, Georgia Tech

Sponsors

IIE: Institute of Industrial Engineers
ASA: American Statistical Association
IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IEEE-CS\DATC: The IEEE Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
NIST: National Institute of Standards and Technology
(SCS): The Society for Modeling and Simulation International
INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation

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Winter Simulation Conference

Publication History

Published: 03 December 2006

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WSC06

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IIE
ASA
IEICE ESS
IEEE-CS\DATC
SIGSIM
NIST
(SCS)
INFORMS-CS

WSC06: Winter Simulation Conference 2006

December 3 - 6, 2006

California, Monterey

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WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Cited By

Lee LChew ELi H(2011)Multi-objective COMPASS for discrete optimization via simulationProceedings of the Winter Simulation Conference10.5555/2431518.2432004(4070-4079)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2432004
Chen ELee L(2009)A multi-objective selection procedure of determining a Pareto setComputers and Operations Research10.1016/j.cor.2008.06.00336:6(1872-1879)Online publication date: 1-Jun-2009
https://dl.acm.org/doi/10.1016/j.cor.2008.06.003
Szechtman RYücesan EJefferson TFowler JIngalls R(2008)A new perspective on feasibility determinationProceedings of the 40th Conference on Winter Simulation10.5555/1516744.1516803(273-280)Online publication date: 7-Dec-2008
https://dl.acm.org/doi/10.5555/1516744.1516803

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