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A combined procedure for optimization via simulation

Authors:

Juta Pichitlamken,

Barry L. NelsonAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 13, Issue 2

Pages 155 - 179

https://doi.org/10.1145/858481.858485

Published: 01 April 2003 Publication History

Abstract

We propose an optimization-via-simulation algorithm for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables may be subject to deterministic linear integer constraints. Our approach---which consists of a global guidance system, a selection-of-the-best procedure, and local improvement---is globally convergent under very mild conditions.

References

[1]

Alrefaei, M. H. and Andradóttir, S. 1999. A simulated annealing algorithm with constant temperature for discrete stochastic optimization. Manage. Sci. 45, 748--764.

Digital Library

[2]

Andradóttir, S. 1995. A method for discrete stochastic optimization. Manage. Sci. 41, 1946--1961.

Digital Library

[3]

Andradóttir, S. 1996. A global search method for discrete stochastic optimization. SIAM J. Optim. 6, 513--530.

[4]

Andradóttir, S. 1998. Simulation optimization. In Handbook of Simulation. J. Banks, Ed. Wiley, New York.

[5]

Andradóttir, S. 1999. Accelerating the convergence of random search methods for discrete stochastic optimization. ACM Trans. Model. Comput. Simulat. 9, 349--380.

Digital Library

[6]

Boesel, J. 1999. Search and selection for large-scale stochastic optimization. Ph.D. Dissertation, Department of Industrial Engineering and Management Sciences, Northwestern Univ., Evanston, Ill.

Digital Library

[7]

Boesel, J., Nelson, B. L., and Ishii, N. 2003a. A framework for simulation-optimization software. IIE Trans. 35, 3, 221--230.

[8]

Boesel J., Nelson, B. L., and Kim, S.-H. 2003b. Using ranking and selection to 'clean up' after simulation optimization. Oper. Res., forthcoming.

Digital Library

[9]

Buzacott, J. A. and Shantikumar, J. G. 1993. Stochastic Models of Manufacturing Systems. Prentice-Hall, Englewood Cliffs, N.J.

[10]

Fu, M. C. 1994. Optimization via simulation: A review. Ann. Oper. Res. 53, 199--247.

[11]

Fu, M. C. 2002. Optimization for simulation: Theory vs. practice. INFORMS J. Comput. 14, 192--215.

Digital Library

[12]

Gelfand, S. B. and Mitter, S. K. 1989. Simulated annealing with noisy or imprecise energy measurements. J. Optim. Theory Appl. 62, 49--62.

Digital Library

[13]

Goldsman, D. and Nelson, B. L. 1998. Comparing systems via simulation. In Handbook of Simulation. J. Banks, Ed. Wiley, New York.

[14]

Gutjahr, W. J. and Pflug, G. Ch. 1996. Simulated annealing for noisy cost functions. J. Glob. Optimiz. 8, 1--13.

[15]

Jacobson, S. H. and Schruben, L. W. 1989. Techniques for simulation response optimization. Oper. Res. Lett. 8, 1--9.

Digital Library

[16]

Koenig, L. W. and Law, A. M. 1985. A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation. Communi. Stat.: Simul. Comput. 14, 719--734.

[17]

Pichitlamken, J. 2002. A combined procedure for optimization via simulation. Ph.D. Dissertation, Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois.

Digital Library

[18]

Pichitlamken, J. and Nelson, B. L. 2001. Selection-of-the-best procedures for optimization via simulation. In Proceedings of the 2001 Winter Simulation Conference (Arlington, Va, Dec.) B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, Eds. IEEE. Piscataway, N.J., 401--407.

Digital Library

[19]

Shi, L. and Chen, C.-H. 2000. A new algorithm for stochastic discrete resource allocation optimization. J. Disc. Event Dyn. Syst. 10, 271--294.

Digital Library

[20]

Shi, L. and Ólafsson, S. 2000. Nested partitions method for stochastic optimization. Meth. Comput. Appl. Prob. 2, 271--291.

Digital Library

[21]

Shi, L., Ólafsson, S., and Chen, Q. 2001. An optimization framework for product design. Manage. Sci. 47, 1681--1692.

Digital Library

[22]

Smith R. L. 1984. Efficient Monte Carlo procedures for generating points uniformly distributed over bounded regions. Oper. Res. 32, 1296--1308.

Digital Library

[23]

Swisher, J. R., Hyden, P. D., Jacobson, S. H., and Schruben, L. W. 2004. A survey of recent advances in discrete input parameter discrete-event simulation optimization. IIE Trans., forthcoming.

[24]

Wolsey, L. A. 1998. Integer Programming. Wiley, New York.

[25]

Yan, D. and Mukai, H. 1992. Stochastic Discrete Optimization. SIAM J. Cont. Optim. 30, 594--612.

Digital Library

Cited By

Wang XMatta AGeng NZhou LJiang Z(2025)Simulation-based emergency department staffing and scheduling optimization considering part-time work shiftsEuropean Journal of Operational Research10.1016/j.ejor.2024.09.020321:2(631-643)Online publication date: Mar-2025
https://doi.org/10.1016/j.ejor.2024.09.020
Bhosekar AEkşioğlu SIşık TAllen R(2021)A discrete event simulation model for coordinating inventory management and material handling in hospitalsAnnals of Operations Research10.1007/s10479-020-03865-5320:2(603-630)Online publication date: 2-Jan-2021
https://doi.org/10.1007/s10479-020-03865-5
Batur DChoobineh F(2020)Selecting the Best Alternative Based on Its QuantileINFORMS Journal on Computing10.1287/ijoc.2020.096533:2(657-671)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1287/ijoc.2020.0965
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Index Terms

A combined procedure for optimization via simulation
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation

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Published In

cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 13, Issue 2

April 2003

105 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/858481

Issue’s Table of Contents

Copyright © 2003 ACM.

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Publication History

Published: 01 April 2003

Published in TOMACS Volume 13, Issue 2

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Cited By

Wang XMatta AGeng NZhou LJiang Z(2025)Simulation-based emergency department staffing and scheduling optimization considering part-time work shiftsEuropean Journal of Operational Research10.1016/j.ejor.2024.09.020321:2(631-643)Online publication date: Mar-2025
https://doi.org/10.1016/j.ejor.2024.09.020
Bhosekar AEkşioğlu SIşık TAllen R(2021)A discrete event simulation model for coordinating inventory management and material handling in hospitalsAnnals of Operations Research10.1007/s10479-020-03865-5320:2(603-630)Online publication date: 2-Jan-2021
https://doi.org/10.1007/s10479-020-03865-5
Batur DChoobineh F(2020)Selecting the Best Alternative Based on Its QuantileINFORMS Journal on Computing10.1287/ijoc.2020.096533:2(657-671)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1287/ijoc.2020.0965
Song JQiu YXu JYang F(2019)Multi-fidelity sampling for efficient simulation-based decision making in manufacturing managementIISE Transactions10.1080/24725854.2019.157695151:7(792-805)Online publication date: 28-May-2019
https://doi.org/10.1080/24725854.2019.1576951
Karatas MOnggo B(2019)OPTIMISING THE BARRIER COVERAGE OF A WIRELESS SENSOR NETWORK WITH HUB-AND-SPOKE TOPOLOGY USING MATHEMATICAL AND SIMULATION MODELSComputers & Operations Research10.1016/j.cor.2019.02.007Online publication date: Feb-2019
https://doi.org/10.1016/j.cor.2019.02.007
Chen QZhao LFransoo JLi Z(2019)Dual-mode inventory management under a chance credit constraintOR Spectrum10.1007/s00291-018-0532-441:1(147-178)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s00291-018-0532-4
Eckman DHenderson S(2018)Reusing Search Data in Ranking and SelectionACM Transactions on Modeling and Computer Simulation10.1145/317050328:3(1-15)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3170503
Zhang QZhong LGao SLi X(2018)Optimizing HIV Interventions for Multiplex Social Networks via Partition-Based Random SearchIEEE Transactions on Cybernetics10.1109/TCYB.2018.285361148:12(3411-3419)Online publication date: Dec-2018
https://doi.org/10.1109/TCYB.2018.2853611
Weiss SSchwarz JStolletz R(2018)The buffer allocation problem in production lines: Formulations, solution methods, and instancesIISE Transactions10.1080/24725854.2018.1442031(1-30)Online publication date: 22-Feb-2018
https://doi.org/10.1080/24725854.2018.1442031
Fu MHenderson S(2017)History of seeking better solutions, aka simulation optimizationProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242192(1-27)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242192
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