An epsilon-constraint method for integer-ordered bi-objective simulation optimization. Cooper, K., Hunter, S. R., & Nagaraj, K. In Chan, W. K. V., D'Ambrogio, A., Zacharewicz, G., Mustafee, N., Wainer, G., & Page, E., editors, *Proceedings of the 2017 Winter Simulation Conference*, pages 2303–2314, Piscataway, NJ, 2017. Institute of Electrical and Electronics Engineers, Inc..

Paper doi abstract bibtex 2 downloads

Paper doi abstract bibtex 2 downloads

Consider the context of integer-ordered bi-objective simulation optimization, in which the feasible region is a finite subset of the integer lattice. We propose a retrospective approximation (RA) framework to identify a local Pareto set that involves solving a sequence of sample-path bi-objective optimization problems at increasing sample sizes. We apply the epsilon-constraint method to each sample-path bi-objective optimization problem, thus solving a sequence of constrained single-objective problems in each RA iteration. We solve each constrained single-objective optimization problem using the SPLINE algorithm, thus exploiting gradient-based information. In early RA iterations, when sample sizes are small and standard errors are relatively large, we provide only a rough characterization of the Pareto set by making the number of epsilon-constraint problems a function of the standard error. As the RA algorithm progresses, the granularity of the characterization increases until we solve as many epsilon-constraint problems as there are points in the (finite) image of the local Pareto set. Our algorithm displays promising numerical performance.

@inproceedings{2017coohunnagWSC, Year = {2017}, Author = {K. Cooper and S. R. Hunter and K. Nagaraj}, Title = {An epsilon-constraint method for integer-ordered bi-objective simulation optimization}, Booktitle = {Proceedings of the 2017 Winter Simulation Conference}, Editor = {W. K. V. Chan and A. {D'Ambrogio} and G. Zacharewicz and N. Mustafee and G. Wainer and E. Page}, Publisher = {Institute of Electrical and Electronics Engineers, Inc.}, Address = {Piscataway, NJ}, doi = {10.1109/WSC.2017.8247961}, pages = {2303--2314}, url = {http://web.ics.purdue.edu/~hunter63/PAPERS/pre2017coohunnagWSC.pdf}, abstract = {Consider the context of integer-ordered bi-objective simulation optimization, in which the feasible region is a finite subset of the integer lattice. We propose a retrospective approximation (RA) framework to identify a local Pareto set that involves solving a sequence of sample-path bi-objective optimization problems at increasing sample sizes. We apply the epsilon-constraint method to each sample-path bi-objective optimization problem, thus solving a sequence of constrained single-objective problems in each RA iteration. We solve each constrained single-objective optimization problem using the SPLINE algorithm, thus exploiting gradient-based information. In early RA iterations, when sample sizes are small and standard errors are relatively large, we provide only a rough characterization of the Pareto set by making the number of epsilon-constraint problems a function of the standard error. As the RA algorithm progresses, the granularity of the characterization increases until we solve as many epsilon-constraint problems as there are points in the (finite) image of the local Pareto set. Our algorithm displays promising numerical performance.}, keywords = {simulation optimization > multi-objective > integer-ordered}}

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We propose a retrospective approximation (RA) framework to identify a local Pareto set that involves solving a sequence of sample-path bi-objective optimization problems at increasing sample sizes. We apply the epsilon-constraint method to each sample-path bi-objective optimization problem, thus solving a sequence of constrained single-objective problems in each RA iteration. We solve each constrained single-objective optimization problem using the SPLINE algorithm, thus exploiting gradient-based information. In early RA iterations, when sample sizes are small and standard errors are relatively large, we provide only a rough characterization of the Pareto set by making the number of epsilon-constraint problems a function of the standard error. As the RA algorithm progresses, the granularity of the characterization increases until we solve as many epsilon-constraint problems as there are points in the (finite) image of the local Pareto set. 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