How to cite this paper
Moghaddam, S & Mahlooji, M. (2016). Robust simulation optimization using φ-divergence.International Journal of Industrial Engineering Computations , 7(4), 517-534.
Refrences
Ajdari, A., & Mahlooji, H. (2014). An adaptive exploration-exploitation algorithm for constructing metamodels in random simulation using a novel sequential experimental design. Communications in Statistics-Simulation and Computation, 43(5), 947-968.
Ankenman, B., Nelson, B. L., & Staum, J. (2010). Stochastic kriging for simulation metamodeling. Operations research, 58(2), 371-382.
Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., & Rennen, G. (2013). Robust solutions of optimization problems affected by uncertain probabilities. Management Science, 59(2), 341-357.
Ben-Tal, A., Den Hertog, D., & Vial, J.-P. (2015). Deriving robust counterparts of nonlinear uncertain inequalities. Mathematical programming, 149(1-2), 265-299.
Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and applications of robust optimization. SIAM review, 53(3), 464-501.
Bertsimas, D., Nohadani, O., & Teo, K. M. (2010). Robust optimization for unconstrained simulation-based problems. Operations research, 58(1), 161-178.
Bhatnagar, S., Mishra, V. K., & Hemachandra, N. (2011). Stochastic algorithms for discrete parameter simulation optimization. Automation Science and Engineering, IEEE Transactions on, 8(4), 780-793.
Birge, J. R., & Wets, R. J.-B. (1986). Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse: Springer.
Calafiore, G. C. (2007). Ambiguous risk measures and optimal robust portfolios. SIAM Journal on Optimization, 18(3), 853-877.
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.
Cramer, A. M., Sudhoff, S. D., & Zivi, E. L. (2009). Evolutionary algorithms for minimax problems in robust design. Evolutionary Computation, IEEE Transactions on, 13(2), 444-453.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2009). Robust simulation-optimization using metamodels. Paper presented at the Winter Simulation Conference.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2010a). Parametric and distribution-free bootstrapping in robust simulation-optimization. Paper presented at the Proceedings of the Winter Simulation Conference.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2010b). Robust optimization in simulation: Taguchi and response surface methodology. International Journal of Production Economics, 125(1), 52-59.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2012). Robust optimization in simulation: Taguchi and Krige combined. INFORMS Journal on Computing, 24(3), 471-484.
Dellino, G., & Meloni, C. (2015). Metamodel Variability in Simulation-Optimization: a Bootstrap Analysis.
Gabrel, V., Murat, C., & Thiele, A. (2014). Recent advances in robust optimization: An overview. European journal of operational research, 235(3), 471-483.
Hurrion, R. (1997). An example of simulation optimisation using a neural network metamodel: finding the optimum number of kanbans in a manufacturing system. Journal of the Operational Research Society, 48(11), 1105-1112.
Kim, S.-H., & Nelson, B. L. (2007). Recent advances in ranking and selection. Paper presented at the Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come.
Klabjan, D., Simchi‐Levi, D., & Song, M. (2013). Robust Stochastic Lot‐Sizing by Means of Histograms. Production and Operations Management, 22(3), 691-710.
Kleijnen, J. P. (2009). Kriging metamodeling in simulation: A review. European journal of operational research, 192(3), 707-716.
Kleijnen, J. P. (2014). Simulation-optimization via Kriging and bootstrapping: a survey. Journal of Simulation, 8(4), 241-250.
Kleijnen, J. P. (2015). Regression and kriging metamodels with their experimental designs in simulation: review (Vol. 35): Tilburg: CentER, Center for Economic Research.
Kleijnen, J. P., Beers, W. v., & Nieuwenhuyse, I. v. (2010). Constrained optimization in expensive simulation: Novel approach. European journal of operational research, 202(1), 164-174.
Kleijnen, J. P., Mehdad, E., & van Beers, W. (2012). Convex and monotonic bootstrapped kriging. Paper presented at the Proceedings of the Winter Simulation Conference.
Kovach, J., & Cho, B. R. (2009). A D-optimal design approach to constrained multiresponse robust design with prioritized mean and variance considerations. Computers & Industrial Engineering, 57(1), 237-245.
Lee, K.-H., Park, G.-J., & Joo, W.-S. (2006). A global robust optimization using kriging based approximation model. JSME International Journal, Series C: Mechanical Systems Machine Elements & Manufacturing, 49(3), 779-803.
Lin, R.-C., Sir, M. Y., & Pasupathy, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services. Omega, 41(5), 881-892.
Liu, H., & Maghsoodloo, S. (2011). Simulation optimization based on taylor kriging and evolutionary algorithm. Applied Soft Computing, 11(4), 3451-3462.
Lung, R. I., & Dumitrescu, D. (2011). A new evolutionary approach to minimax problems. Paper presented at the Evolutionary Computation (CEC), 2011 IEEE Congress on.
Marzat, J., Walter, E., & Piet-Lahanier, H. (2013). Worst-case global optimization of black-box functions through Kriging and relaxation. Journal of Global Optimization, 55(4), 707-727.
Myers, W. R., & Montgomery, D. C. (2003). Response surface methodology. Encycl Biopharm Stat, 1, 858-869.
Nezhad, A. M., & Mahlooji, H. (2013). An artificial neural network meta-model for constrained simulation optimization. Journal of the Operational Research Society, 65(8), 1232-1244.
Pardo, L. (2005). Statistical inference based on divergence measures: CRC Press.
Quan, N., Yin, J., Ng, S. H., & Lee, L. H. (2013). Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints. Iie Transactions, 45(7), 763-780.
Rustem, B., & Howe, M. (2009). Algorithms for worst-case design and applications to risk management: Princeton University Press.
Shapiro, A., & Ahmed, S. (2004). On a class of minimax stochastic programs. SIAM Journal on Optimization, 14(4), 1237-1249.
Shapiro, A., & Kleywegt, A. (2002). Minimax analysis of stochastic problems. Optimization Methods and Software, 17(3), 523-542.
Simpson, T. W., Poplinski, J., Koch, P. N., & Allen, J. K. (2001). Metamodels for computer-based engineering design: survey and recommendations. Engineering with computers, 17(2), 129-150.
Stinstra, E., & Den Hertog, D. (2008). Robust optimization using computer experiments. European journal of operational research, 191(3), 816-837.
Wang, Z., Glynn, P., & Ye, Y. (2009). Likelihood robust optimization for data-driven newsvendor problems: Working paper.
Yang, F. (2010). Neural network metamodeling for cycle time-throughput profiles in manufacturing. European journal of operational research, 205(1), 172-185.
Yanikoglu, I., & den Hertog, D. (2012). Safe approximations of ambiguous chance constraints using historical data. INFORMS Journal on Computing, 25(4), 666-681.
Zhou, A., & Zhang, Q. (2010). A surrogate-assisted evolutionary algorithm for minimax optimization. Paper presented at the Evolutionary Computation (CEC), 2010 IEEE Congress on.
Ankenman, B., Nelson, B. L., & Staum, J. (2010). Stochastic kriging for simulation metamodeling. Operations research, 58(2), 371-382.
Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., & Rennen, G. (2013). Robust solutions of optimization problems affected by uncertain probabilities. Management Science, 59(2), 341-357.
Ben-Tal, A., Den Hertog, D., & Vial, J.-P. (2015). Deriving robust counterparts of nonlinear uncertain inequalities. Mathematical programming, 149(1-2), 265-299.
Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and applications of robust optimization. SIAM review, 53(3), 464-501.
Bertsimas, D., Nohadani, O., & Teo, K. M. (2010). Robust optimization for unconstrained simulation-based problems. Operations research, 58(1), 161-178.
Bhatnagar, S., Mishra, V. K., & Hemachandra, N. (2011). Stochastic algorithms for discrete parameter simulation optimization. Automation Science and Engineering, IEEE Transactions on, 8(4), 780-793.
Birge, J. R., & Wets, R. J.-B. (1986). Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse: Springer.
Calafiore, G. C. (2007). Ambiguous risk measures and optimal robust portfolios. SIAM Journal on Optimization, 18(3), 853-877.
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.
Cramer, A. M., Sudhoff, S. D., & Zivi, E. L. (2009). Evolutionary algorithms for minimax problems in robust design. Evolutionary Computation, IEEE Transactions on, 13(2), 444-453.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2009). Robust simulation-optimization using metamodels. Paper presented at the Winter Simulation Conference.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2010a). Parametric and distribution-free bootstrapping in robust simulation-optimization. Paper presented at the Proceedings of the Winter Simulation Conference.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2010b). Robust optimization in simulation: Taguchi and response surface methodology. International Journal of Production Economics, 125(1), 52-59.
Dellino, G., Kleijnen, J. P., & Meloni, C. (2012). Robust optimization in simulation: Taguchi and Krige combined. INFORMS Journal on Computing, 24(3), 471-484.
Dellino, G., & Meloni, C. (2015). Metamodel Variability in Simulation-Optimization: a Bootstrap Analysis.
Gabrel, V., Murat, C., & Thiele, A. (2014). Recent advances in robust optimization: An overview. European journal of operational research, 235(3), 471-483.
Hurrion, R. (1997). An example of simulation optimisation using a neural network metamodel: finding the optimum number of kanbans in a manufacturing system. Journal of the Operational Research Society, 48(11), 1105-1112.
Kim, S.-H., & Nelson, B. L. (2007). Recent advances in ranking and selection. Paper presented at the Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come.
Klabjan, D., Simchi‐Levi, D., & Song, M. (2013). Robust Stochastic Lot‐Sizing by Means of Histograms. Production and Operations Management, 22(3), 691-710.
Kleijnen, J. P. (2009). Kriging metamodeling in simulation: A review. European journal of operational research, 192(3), 707-716.
Kleijnen, J. P. (2014). Simulation-optimization via Kriging and bootstrapping: a survey. Journal of Simulation, 8(4), 241-250.
Kleijnen, J. P. (2015). Regression and kriging metamodels with their experimental designs in simulation: review (Vol. 35): Tilburg: CentER, Center for Economic Research.
Kleijnen, J. P., Beers, W. v., & Nieuwenhuyse, I. v. (2010). Constrained optimization in expensive simulation: Novel approach. European journal of operational research, 202(1), 164-174.
Kleijnen, J. P., Mehdad, E., & van Beers, W. (2012). Convex and monotonic bootstrapped kriging. Paper presented at the Proceedings of the Winter Simulation Conference.
Kovach, J., & Cho, B. R. (2009). A D-optimal design approach to constrained multiresponse robust design with prioritized mean and variance considerations. Computers & Industrial Engineering, 57(1), 237-245.
Lee, K.-H., Park, G.-J., & Joo, W.-S. (2006). A global robust optimization using kriging based approximation model. JSME International Journal, Series C: Mechanical Systems Machine Elements & Manufacturing, 49(3), 779-803.
Lin, R.-C., Sir, M. Y., & Pasupathy, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services. Omega, 41(5), 881-892.
Liu, H., & Maghsoodloo, S. (2011). Simulation optimization based on taylor kriging and evolutionary algorithm. Applied Soft Computing, 11(4), 3451-3462.
Lung, R. I., & Dumitrescu, D. (2011). A new evolutionary approach to minimax problems. Paper presented at the Evolutionary Computation (CEC), 2011 IEEE Congress on.
Marzat, J., Walter, E., & Piet-Lahanier, H. (2013). Worst-case global optimization of black-box functions through Kriging and relaxation. Journal of Global Optimization, 55(4), 707-727.
Myers, W. R., & Montgomery, D. C. (2003). Response surface methodology. Encycl Biopharm Stat, 1, 858-869.
Nezhad, A. M., & Mahlooji, H. (2013). An artificial neural network meta-model for constrained simulation optimization. Journal of the Operational Research Society, 65(8), 1232-1244.
Pardo, L. (2005). Statistical inference based on divergence measures: CRC Press.
Quan, N., Yin, J., Ng, S. H., & Lee, L. H. (2013). Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints. Iie Transactions, 45(7), 763-780.
Rustem, B., & Howe, M. (2009). Algorithms for worst-case design and applications to risk management: Princeton University Press.
Shapiro, A., & Ahmed, S. (2004). On a class of minimax stochastic programs. SIAM Journal on Optimization, 14(4), 1237-1249.
Shapiro, A., & Kleywegt, A. (2002). Minimax analysis of stochastic problems. Optimization Methods and Software, 17(3), 523-542.
Simpson, T. W., Poplinski, J., Koch, P. N., & Allen, J. K. (2001). Metamodels for computer-based engineering design: survey and recommendations. Engineering with computers, 17(2), 129-150.
Stinstra, E., & Den Hertog, D. (2008). Robust optimization using computer experiments. European journal of operational research, 191(3), 816-837.
Wang, Z., Glynn, P., & Ye, Y. (2009). Likelihood robust optimization for data-driven newsvendor problems: Working paper.
Yang, F. (2010). Neural network metamodeling for cycle time-throughput profiles in manufacturing. European journal of operational research, 205(1), 172-185.
Yanikoglu, I., & den Hertog, D. (2012). Safe approximations of ambiguous chance constraints using historical data. INFORMS Journal on Computing, 25(4), 666-681.
Zhou, A., & Zhang, Q. (2010). A surrogate-assisted evolutionary algorithm for minimax optimization. Paper presented at the Evolutionary Computation (CEC), 2010 IEEE Congress on.