A tutorial on simulation optimization

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	1	Azadivar, F. and Talavage, J.J., 1980. Optimization of stochastic simulation models, Mathematics and Computers in Simulation, 22:231-241.
	2	Farhad Azadivar , Young-Hae Lee, Optimization of discrete variable stochastic systems by computer simulation, Mathematics and Computers in Simulation, v.30 n.4, p.331-345, September 1988 [doi>10.1016/S0378-4754(98)90004-0]
	3	William Ernest Biles, A gradient—regression search procedure for simulation experimentation, Proceedings of the 7th conference on Winter simulation, p.491-497, January 14-16, 1974, Washington, DC [doi>10.1145/800290.811307]
	4	Biles, W. E., 1975. A response surface method for experimental optimization of multiresponse processes, Industrial and Engineering Chemistry: Process De,~i g n and Development, 14:152-158.
	5	Biles, W. E., 1977. Optimization of multiple objective computer simulation" A non linear goal programnfing approach, Proceedings of AIDS Conference, Chicago, 250-252.
	6	Biles, W. E., and Swain, J.J., 1980. Optimization and industrial experimentation, Wiley lnterscience, New York.
	7	Box, E. P., 1956. A new method for constraJined optimization and a comparison with other methods, The Computer Journal, 8:42-52.
	8	Clayton, E. R., W.E. Weber, and B.W. Taylor, 1982. A goal programming approach to the optimization of multiresponse simulation models, liE Transactions, 14: 282-287.
	9	Andrew F. Daughety , Mark A. Turnquist, Simulation optimization using response surfaces based on spline approximations, Proceedings of the 10th conference on Winter simulation, p.183-193, December 04-06, 1978, Miami Beach, FL
	10	Eglese, R. W., 1990. Simulated annealing: a tool for operational research, European Journal of Operational Research, 46:271-281.
	11	Peter W. Glynn, Optimization of stochastic systems, Proceedings of the 18th conference on Winter simulation, p.52-59, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318260]
	12	Peter W. Glynn, Likelilood ratio gradient estimation: an overview, Proceedings of the 19th conference on Winter simulation, p.366-375, December 14-16, 1987, Atlanta, Georgia, United States [doi>10.1145/318371.318612]
	13	Peter W. Glynn, Stochastic approximation for Monte Carlo optimization, Proceedings of the 18th conference on Winter simulation, p.356-365, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318459]
	14	P. Heidelberger, 1986. Limitations of infinitesimal perturbation analysis, IBM Research Report RC 11891, IBM Watson Research Labs, Yorktown Heights, NY.
	15	Y. C. Ho, Perturbation analysis methods for discrete event dynamical systems and simulations, Proceedings of the 16th conference on Winter simulation, p.170-173, December 28-30, 1984, Dallas, TX
	16	Ho, Y. C., and Cao, X. R., 1991. Perturbation analysis of discrete event dynamic systems, Kluwer Academic Publishers.
	17	Ho, Y. C., Cao, X. R., and Cassandras, C., 1983. Infinitesimal and finite perturbation analysis of queuing networks, Automatica, 19:439-445.
	18	Ho, T. C., Suri, R., Cao, X. R., Diehl, G. W., Dille, J. W., and Zazanis, M., 1984. Optimization of large multiclass (non-product-form) queuing networks using perturbation analysis, Large Scale Systems, 7:165-180.
	19	Jacobson, S. H., and L. W. Schruben, 1989. Techniques for simulation response optimization, Operations Research Letters, 8:1-9.
	20	Jacobson, S. H., and Schruben, L. W., 1991. A simulation optimization procedure using harmonic analysis, working paper.
	21	Kiefer, J., and Wolfowitz, J., 1952. Stochastic estimation of the maximum of a regression function, Annals of Mathematical Statistics, 23:462-446.
	22	Marc S. Meketon, Optimization in simulation: a survey of recent results, Proceedings of the 19th conference on Winter simulation, p.58-67, December 14-16, 1987, Atlanta, Georgia, United States [doi>10.1145/318371.318384]
	23	Mollaghasemi, M., Evans, G.W., and Biles, W. E., 1991. An approach for optimizing multiple response simulation models, Proceedings of 13th Annual Conference in Computers and Industrial Engineering.
	24	Mollaghasemi, M., and Evans, G.W., 1992. A methodology for stochastic optimization of multiple response simulation models, working paper.
	25	J.A. Nelder and R. Mead, 1976. A simplex method for function minimization, Computer Journal, 7, 308-313 (1965).9.
	26	Claude Dennis Pegden , Michael P. Gately, Decision optimization for GASP IV simulation models, Proceedings of the 9th conference on Winter simulation, p.126-133, December 05-07, 1977, Gaitherburg, Maryland, United States
	27	S. Prakash , R. E. Shannon, Intelligent back end of a goal directed simulation environment for discrete-part manufacturing, Proceedings of the 21st conference on Winter simulation, p.883-891, December 04-06, 1989, Washington, D.C., United States [doi>10.1145/76738.76850]
	28	Rees, L. P., E. R. Calyton, and B. W. Taylor, 1985. Solving multiple response simulation models using response surface methodology within a lexicographic goal programming framework, liE Transactions, 17:47-57.
	29	Robbins, H., and Monro, S., 1951. A stochastic approximation method, Annals of Mathematical Statistics, 22: 400-407.
	30	R. Y. Rubinstein, Sensitivity analysis and performance extrapolation for computer simulation models, Operations Research, v.37 n.1, p.72-81, Jan.-Feb. 1989 [doi>10.1287/opre.37.1.72]
	31	Safizadeh, M. H., 1990. Optimization in simulation: current issues and the future outlook, Naval Research Logistics, 37: 807-825.
	32	Safizadeh, M. H. and Signorila, R., 1992. Optimization of simulation via quasi-newton methods, working paper.
	33	Lee Schruben, Simulation optimization using frequency domain methods, Proceedings of the 18th conference on Winter simulation, p.366-369, December 08-10, 1986, Washington, D.C., United States [doi>10.1145/318242.318460]
	34	Lee W. Schruben , Vincent James Cogliano, Simulation sensitivity analysis: A frequency domain approach, Proceedings of the 13th conference on Winter simulation, p.455-459, December 09-11, 1981, Atlanta, Georgia, United States
	35	D.E. Smith, Automatic optimum-seeking program for digital simulation, Simulation 27:27-32.
	36	Suri, R., 1983. Infmitesimal perturbation analysis of discrete even dynamic systems: a general theory, Proceedings of the Twenty-Second IEEE Conference on Decision and Control, 1030-1038.
	37	Teleb, R., and F. Azadivar, 1992. A methodology for solving multi-objective simulation-optimization problems, European Journal of Operational Research, to appear.
	38	James R. Wilson, Future directions in response surface methodology for simulation, Proceedings of the 19th conference on Winter simulation, p.378-381, December 14-16, 1987, Atlanta, Georgia, United States [doi>10.1145/318371.318615]