|
Ahmed, M. A., Alkhamis, T. M., & Hasan, M. (1997). Optimizing discrete stochastic systems using simulated annealing and simulation. Computers & Industrial Engineering, 32(4), 823-836. Anderson, E. J., & Ferris, M. C. (2001). A direct search algorithm for optimization with noisy function evaluations. SIAM Journal on Optimization, 11(3), 837-857. Andrieu, L., Cohen, G., & Vázquez-Abad, F. J. (2011). Gradient-based simulation optimization under probability constraints. European Journal of Operational Research, 212(2), 345-351. Angün, E., & Kleijnen, J. (2012). An asymptotic test of optimality conditions in multiresponse simulation optimization. INFORMS Journal on Computing, 24(1), 53-65. Banks, J., Carson, J. S., & Nelson, B. L. (2000). Discrete-Event System Simulation. 3rd ed. Prentice Hall, Englewood Cliffs, New Jersey. Barton, R. R., & Ivey Jr, J. S. (1996). Nelder-Mead simplex modifications for simulation optimization. Management Science, 42(7), 954-973. Bäck, T., & Schwefel, H. P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), 1-23. Benveniste, A., Métivier, M., & Priouret, P. (2012). Adaptive Algorithms and Stochastic Approximations. Springer, New York. Bhatnagar, S., Hemachandra, N., & Mishra, V. K. (2011). Stochastic approximation algorithms for constrained optimization via simulation. ACM Transactions on Modeling and Computer Simulation, 21(3), 1-22.
Blum, J. R. (1954). Approximation methods which converge with probability one. The Annals of Mathematical Statistics, 25(2), 382-386. Borkar, V. S. (2008). Stochastic Approximation. Cambridge Books, Cambridge. Bratley, P., Fox, B. L., & Schrage, L. E. (1983). A Guide to Simulation, vol. 2. Springer-Verlag, New York. Brooks, S. P., & Morgan, B. J. (1995). Optimization using simulated annealing. The Statistician, 44(2), 241-257. Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51. Chang, K.-H. (2012). Stochastic Nelder–Mead simplex method–A new globally convergent direct search method for simulation optimization. European Journal of Operational Research, 220(3), 684-694. Chen, C.-H., & Lee, L.-H. (2010). Stochastic Simulation Optimization: An Optimal Computing Budget Allocation. World Scientific, Singapore. Chen, C.-H., Lin, J., Yücesan, E., & Chick, S. E. (2000). Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems, 10(3), 251-270. Chen, M.-C., & Tsai, D.-M. (1996). Simulation optimization through direct search for multi-objective manufacturing systems. Production Planning & Control, 7(6), 554-565. Cheng, R. C. H. (1984). Antithetic variate methods for simulations of processes with peaks and troughs. European Journal of Operational Research, 15(2), 227-236. Chick, S. E. (1997). Selecting the best system: A decision-theoretic approach. In Proceedings of the 29th Conference on Winter Simulation, 326-333.
Dorigo, M., & Birattari, M. (2010). Encyclopedia of Machine Learning. Springer, New York. Feo, T. A., & Resende, M. G. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6(2), 109-133. Fu, M. C. (2006). Gradient estimation. Handbooks in Operations Research and Management Science, 13, 575-616. Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13(5), 533-549. Glynn, P. W. (1994). Efficiency improvement techniques. Annals of Operations Research, 53(1), 175-197. Hansen, P., & Mladenović, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130(3), 449-467. Heidelberger, P. (1995). Fast simulation of rare events in queueing and reliability models. ACM Transactions on Modeling and Computer Simulation, 5(1), 43-85. Hooke, R., & Jeeves, T. A. (1961). “Direct Search” solution of numerical and statistical problems. Journal of the ACM, 8(2), 212-229. Humphrey, D. G., & Wilson, J. R. (2000). A revised simplex search procedure for stochastic simulation response surface optimization. INFORMS Journal on Computing, 12(4), 272-283. Khuri, A. I., & Cornell, J. A. (1996). Response Surfaces: Designs and Analyses. CRC Press, Boca Raton. Kiefer, J., & Wolfowitz, J. (1952). Stochastic estimation of the maximum of a regression function. The Annals of Mathematical Statistics, 23(3), 462-466. Kolda, T. G., Lewis, R. M., & Torczon, V. (2003). Optimization by direct search: New perspectives on some classical and modern methods. SIAM Review, 45(3), 385-482. Kushner, H. J., & Clark, D. S. (1978). Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer Science & Business Media, New York. Kushner, H. J., & Yin, G. G. (1997). Stochastic Approximation Algorithms and Applications. Springer-Verlag, New York. Law, A. M., Kelton, W. D., & Kelton, W. D. (1991). Simulation Modeling and Analysis, vol. 2. McGraw-Hill, New York. L'Ecuyer, P. (1994). Efficiency improvement and variance reduction. In Proceedings of the 26th Conference on Winter Simulation, 122-132. Leonidas, S., Pitsoulis, L., & Resende, M. G. C. (2002). Handbook of Applied Optimization. Oxford University Press, Oxford. Lewis, R. M., Torczon, V., & Trosset, M. W. (2000). Direct search methods: then and now. Journal of Computational and Applied Mathematics, 124(1), 191-207. Martin, O., Otto, S. W., & Felten, E. W. (1991). Large-step Markov chains for the traveling salesman problem. Complex Systems, 5(3), 299-328. Morrice, D. J., & Schruben, L. W. (1989). Simulation sensitivity analysis using frequency domain experiments. In Proceedings of the 21st conference on Winter Simulation, 367-373. Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 3rd ed. John Wiley & Sons., New York. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308-313. Nelson, B. L. (1990). Control variate remedies. Operations Research, 38(6), 974-992. Osman, I. H. & Laporte, G. (1996). Metaheuristics: A bibliography. Annals of Operations Research, 63, 513–623. Price, C. J., Coope, I. D., & Byatt, D. (2002). A convergent variant of the Nelder–Mead algorithm. Journal of Optimization Theory and Applications, 113(1), 5-19. Spall, J. C. (1992). Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. Automatic Control, IEEE Transactions on, 37(3), 332-341. Spall, J. C. (1997). A one-measurement form of simultaneous perturbation stochastic approximation. Automatica, 33(1), 109-112. Spall, J. C. (2003). Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. John Wiley & Sons., New York. Spendley, W. G. R. F. R., Hext, G. R., & Himsworth, F. R. (1962). Sequential application of simplex designs in optimization and evolutionary operation. Technometrics, 4(4), 441-461. Swann, W. H. (1972). Direct search methods. W. Murray ed., Numerical Methods for Unconstrained Optimization. Academic Press, London and New York, 13-28. Tekin, E., & Sabuncuoglu, I. (2004). Simulation optimization: A comprehensive review on theory and applications. IIE Transactions, 36(11), 1067-1081. Voß, S., Martello, S., Osman, I. H., & Roucairol, C. Meta-Heuristics—Advances and Trends in Local Search Paradigms for Optimization. 1st ed. Kluwer Academic Publishers, Dordrecht, the Netherlands. Voudouris, C., & Tsang, E. P. (2003). Guided Local Search. Springer, New York. Wang, I. J., & Spall, J. C. (2008). Stochastic optimization with inequality constraints using simultaneous perturbations and penalty functions. International Journal of Control, 81(8), 1232-1238. Zhu, C., Xu, J., Chen, C.-H., Lee, L.-H., & Hu, J. (2013). Determining the optimal sampling set size for random search. In Proceedings of the 2013 Winter Simulation Conference, 1016-1024.
|