Please use this identifier to cite or link to this item: https://doi.org/10.1109/WSC.2006.323086
Title: Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problems
Authors: Lee, L.H. 
Chew, E.P. 
Teng, S. 
Issue Date: 2006
Source: Lee, L.H.,Chew, E.P.,Teng, S. (2006). Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problems. Proceedings - Winter Simulation Conference : 294-303. ScholarBank@NUS Repository. https://doi.org/10.1109/WSC.2006.323086
Abstract: In this paper, we consider a multi-objective simulation optimization problem with three features: huge solution space, high uncertainty in performance measures, and multi-objective problem which requires a set of nondominated solutions. Our main purpose is to study how to integrate statistical selection with search mechanism to address the above difficulties, and to present a general solution framework for solving such problems. Here due to the multi-objective nature, statistical selection is done by the multi-objective computing budget allocation (MOCBA) procedure. For illustration, MOCBA is integrated with two meta-heuristics: multi-objective evolutionary algorithm (MOEA) and nested partitions (NP) to identify the nondominated solutions for two inventory management case study problems. Results show that, the integrated solution framework has improved both search efficiency and simulation efficiency. Moreover, it is capable of identifying a set of non-dominated solutions with high confidence. © 2006 IEEE.
Source Title: Proceedings - Winter Simulation Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/72340
ISBN: 1424405017
ISSN: 08917736
DOI: 10.1109/WSC.2006.323086
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