Optimizing discrete stochastic systems using simulated annealing and simulation

Computers and Industrial Engineering

Volumn 32, Issue 4, 1997, Pages 823-836

  Ahmed, Mohamed A ^a   Alkhamis, Talal M ^a   Hasan, Merza ^a  

^a KUWAIT UNIVERSITY   (Kuwait)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONSTRAINT THEORY; MATHEMATICAL MODELS; MONTE CARLO METHODS; PARAMETER ESTIMATION; PROBLEM SOLVING; RANDOM PROCESSES; SIMULATED ANNEALING;

MODIFIED REJECTION ACCEPTANCE CRITERION; MULTI ECHELON REPAIRABLE ITEM INVENTORY SYSTEMS (MERIIS);

STOCHASTIC CONTROL SYSTEMS;

EID: 0031221996     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0360-8352(97)00006-5     Document Type: Article

References (40)

1. Gross, D. and Miller, D. R., Multi-echelon repairable item provisioning in a time-varying environment using the randomization technique. Naval Research Logistics Quart., 1984, 31, 347-361.
2. Gross, D., Miller, D. R. and Soland, R. M., A closed queuing network model for multi-echelon repairable item provisioning. IIE Trans., 1983, 15, 344-352.
3. F. Azadivar. A tutorial on simulation optimization. Proc. 1992 Winter Simulation Conf. (Edited by J. J. Swain, D. Goldsman, R. C. Crain and J. R. Wilson), pp. 198-203 (1992).
4. Y. C. Ho. Perturbation analysis methods for discrete event dynamically systems and simulations. Proc. 1984 Winter Simulation Conf., pp. 171-173 (1984).
5. Y. C. Ho and X. R. Cao. Perturbation Analysis of Discrete Event Dynamic Systems. Kluwer Academic, Dordrecht (1991).
6. Jacobson, S. H. and Schruben, L. W., Techniques for simulation response optimization. Operations Res. Lett., 1989, 8, 1-9.
7. L. W. Schruben. Simulation optimization using frequency domain methods. Proc. 1986 Winter Simulation Conf. (Edited by J. R. Wilson, J. O. Henriksen and S. D. Roberts), pp. 366-369 (1986).
8. P. W. Glynn. Likelihood ratio gradient estimation: an overview. Proc. 1987 Winter Simulation Conf. (Edited by A. Thesen, H. Grant and W. David Kelton), pp. 366-375 (1987).
9. R. Y. Rubinstein. Sensitivity analysis and performance extrapolation for computer simulation methods. Operations Res. 37(1), 72-81 (1989).
10. S. Andradottir. A stochastic approximation algorithm with bounded iterates. Technical Report 91-2, Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI (1991).
11. S. Andradottir. A stochastic approximation algorithm with varying bounded. Technical Report 92-5, Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI (1992).
12. P. W. Glynn. Optimization of stochastic systems. Proc. 1986 Winter Simulation Conf. (Edited by J. R. Wilson, J. O. Henriksen and S. D. Roberts), pp. 52-59 (1986).
13. Kiefer, J. and Wolfowitz, J., Stochastic estimation of the maximum of a regression function. Ann. Math. Stat., 1958, 23, 462-466.
14. Robbins, H. and Monroe, S., On a stochastic approximation technique. Ann. Math. Stat., 1951, 22, 400-407.
15. Ruppert, D., A Newton-Raphson version of the multivariate Robbins-Monro procedure. Ann. Stat., 1985, 13, 236-245.
16. Biles, W. E., A response surface method for experimental optimization of multiresponse processes. Ind. Eng. Chem.: Process Design and Development, 1975, 14, 152-158.
17. Smith, D. E., Automatic optimum-seeking program for digital simulation. Simulation, 1976, 27, 27-32.
18. J. R. Wilson. Future direction in response surface methodology for simulation. Proc. 1987 Winter Simulation Conf. (Edited by A. Thesen, H. Grant and W. David Kelton), pp. 378-381 (1987).
19. M. S. Meketon. Optimization in simulation: a survey of recent results. Proc. 1987 Winter Simulation Conf. (Edited by A. Thesen, H. Grant and W. David Kelton), pp. 58-67 (1987).
20. S. Andradottir. Discrete optimization in simulation: a method and application. In Proc. 1992 Winter Simulation Conf. (Edited by J. J. Swain, D. Goldsman, R. C. Crain and J. R. Wilson), pp. 483-486 (1992).
21. Yan, D. and Mukai, H., Stochastic discrete optimization. SIAM J. Control and Optimization, 1992, 30, 594-612.
22. W. Gong, Y. Ho and W. Zhai. Stochastic comparison algorithm for discrete optimization with estimation. Proc. 31st Conf. on Decision and Control, Tucson, AZ (1992).
23. Haddock, J. and Mittenthal, J., Simulation optimization using simulated annealing. Comput. Ind. Eng., 1992, 20, 387-395.
24. A. A. Bulgak and J. L. Sanders. Integrating a modified simulated annealing algorithm with the simulation of a manufacturing system to optimize buffer sizes in automatic assembly systems. Proc. 1988 Winter Simulation Conf. (Edited by M. Abrams, P. Haigh and J. Comfort), pp. 684-690 (1988).
25. D. G. So and K. A. Dowsland, Simulated annealing: an application to simulation optimization. Paper presented at OR35 Conf., University of York (1993).
26. C. R. Reeves, A genetic algorithm approach to stochastic flowshop sequencing. Technical report, Division of Statistics and Operations Research, School of Mathematics and Information Science, Coventry University (1993).
27. R. B. Cooper. Introduction to Queuing Theory, 2nd edn. North Holland, Amsterdam (1981).
28. D. Gross and C. M. Harris. Fundamentals of Queuing Theory, 2nd edn. Wiley, New York (1985).
29. L. Klenrock. Queuing Systems, Vol. 1. Wiley, New York (1975).
30. Ahmed, M. A., Gross, D. and Miller, D. R., Control variate models for estimating transient performance measures in repairable item systems. Management Sci., 1992, 38, 388-399.
31. Gaver, D. P. and Shedler, G. S., Control variable methods in the simulation of a model of a multiprogrammed computer system. Naval Research Logistic Quart., 1970, 18, 435-450.
32. D. P. Gaver and G. L. Thompson. Programming and Probability Models in Operations Research. Brooks and Cole, Monterey, CA (1973).
33. Wilson, J. R., Variance reduction techniques for digital simulation. Am. J. Math. Management Sci., 1984, 4, 277-312.
34. Kirkpatrick, S., Gelatt, C. D. Jr. and Vecchi, M. P., Optimization by simulated annealing. Science, 1983, 221, 671-680.
35. Corny, V., Thermodynamical approach to the traveling salesman problem, an efficient simulation algorithm. J. Optimization Theory Appl., 1985, 45, 41-51.
36. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A. and Tetter, E., Equation of state calculations by fast computing machines. J. Chem. Phys., 1953, 21, 1087-1092.
37. S. R. White. Concepts of scale in simulated annealing. Proc. IEEE Int. Conf. on Computer Design, Port Chester, pp. 646-651 (1984).
38. Balana, A. R., Gross, D. and Soland, R. M., Optimal provisioning for single-echelon repairable item inventory control in a time-varying environment. IIE Trans., 1989, 21, 202-212.
39. M. A. Crane and A. G. Lemoine. An introduction to the regenerative method for simulation analysis. In Lecture Notes in Control and Information Sciences, Vol. 4, pp. 2-9. Springer-Verlag, New York (1977).
40. Buyukkurt, M. D. and Parlar, M., A comparison of allocation policies in a two-echelon repairable item inventory model. Int. J. Production Economics, 1993, 29, 291-302.