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Optimization of discrete variable stochastic systems by computer simulation

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  • Azadivar, Farhad
  • Lee, Young-Hae

Abstract

A heuristic procedure is developed for determining the optimum values of the decision variables of discrete variable systems whose performances are evaluated by computer simulation. The objective function and some of the constraints of this optimization are various responses of the simulated model. The constrained simplex search method is the basis of this development. However, due to the stochastic nature of the simulation responses, the vertices of the simplex are compared statistically. The algorithm uses a variable simulation run length to minimize the required computer time. The data on the simulation output at each decision point are monitored continuously and, as soon as a statistically reliable comparison among the alternatives can be made, the simulation run at that point is terminated. The whole procedure is developed into an algorithm that can be interfaced with the simulation model built by the analyst. In this paper, the significant aspects of the algorithm and its application to a practical problem as well as the results of the comparison of its performance with respect to two other optimization search methods are presented.

Suggested Citation

  • Azadivar, Farhad & Lee, Young-Hae, 1988. "Optimization of discrete variable stochastic systems by computer simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 30(4), pages 331-345.
    • Handle: RePEc:eee:matcom:v:30:y:1988:i:4:p:331-345
    DOI: 10.1016/S0378-4754(98)90004-0
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    1. Basil A. Kalymon, 1975. "An Optimization Algorithm for a Linear Model of a Simulation System," Management Science, INFORMS, vol. 21(5), pages 516-530, January.
    2. Azadivar, F. & Talavage, J., 1980. "Optimization of stochastic simulation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 22(3), pages 231-241.
    3. Richard L. Nolan & Michael G. Sovereign, 1972. "A Recursive Optimization and Simulation Approach to Analysis with an Application to Transportation Systems," Management Science, INFORMS, vol. 18(12), pages 676-690, August.
    4. James C. Hershauer & Ronald J. Ebert, 1975. "Search and Simulation Selection of a Job-Shop Sequencing Rule," Management Science, INFORMS, vol. 21(7), pages 833-843, March.
    5. Meier, Robert C., 1967. "The Application of Optimum-Seeking Techniques to Simulation Studies: A Preliminary Evaluation*," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 2(1), pages 31-51, March.
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    1. Hachicha, Wafik & Ammeri, Ahmed & Masmoudi, Faouzi & Chachoub, Habib, 2010. "A comprehensive literature classification of simulation optimisation methods," MPRA Paper 27652, University Library of Munich, Germany.
    2. Azadivar, Farhad & Tompkins, George, 1999. "Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach," European Journal of Operational Research, Elsevier, vol. 113(1), pages 169-182, February.
    3. Botes, J. H. F. & Bosch, D. J. & Oosthuizen, L. K., 1996. "A simulation and optimization approach for evaluating irrigation information," Agricultural Systems, Elsevier, vol. 51(2), pages 165-183, June.
    4. Kazmierczak, Richard F., Jr., 1990. "Analyzing Complex Dynamic Bioeconomic Systems Using A Simulation Optimization Technique," 1990 Annual meeting, August 5-8, Vancouver, Canada 270852, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Yang, Taho & Chou, Pohung, 2005. "Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(1), pages 9-21.
    6. Arsham H., 1998. "Techniques for Monte Carlo Optimizing," Monte Carlo Methods and Applications, De Gruyter, vol. 4(3), pages 181-230, December.
    7. Kazmierczak, Richard F., Jr., 1996. "Optimizing Complex Bioeconomic Simulations Using An Efficient Search Heuristic," DAE Research Reports 31661, Louisiana State University, Department of Agricultural Economics and Agribusiness.
    8. Kleijnen, J.P.C. & Biles, B.E., 1999. "A Java-based simulation manager for optimization and response surface methodology in multiple-response parallel simulation," Other publications TiSEM 628a280a-c055-4fa2-9d54-5, Tilburg University, School of Economics and Management.
    9. Oriade, Caleb A. & Dillon, Carl R., 1997. "Developments in biophysical and bioeconomic simulation of agricultural systems: a review," Agricultural Economics, Blackwell, vol. 17(1), pages 45-58, October.

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