Please use this identifier to cite or link to this item:
|Title:||Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problems||Authors:||Lee, L.H.
|Issue Date:||2006||Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 19, 2022
checked on Sep 22, 2022
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.