Paper Info
Title
Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problems
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.
Year
DOI
Venue
2006
10.1109/WSC.2006.323086
Winter Simulation Conference
Keywords
Field
DocType
nondominated solution,general solution framework,multi-objective computing budget allocation,multi-objective simulation-optimization problem,search mechanism,statistical selection,multi-objective simulation optimization problem,multi-objective evolutionary algorithm,multi-objective nature,multi-objective problem,integrated solution framework,huge solution space,statistical analysis
Mathematical optimization,Evolutionary algorithm,Computer science,Budget allocation,Optimization problem,Statistical analysis
Conference
ISBN
Citations 
PageRank 
1-4244-0501-7
5
0.60
References 
Authors
17
3
Name
Order
Citations
PageRank
Loo Hay Lee1115993.96
Ek Peng Chew245944.07
Suyan Teng31136.92