Paper Info
Title
Optimal computing budget allocation for multi-objective simulation models
Abstract
Simulation plays a vital role in identifying the best system design from among a set of competing designs. To improve simulation efficiency, ranking and selection techniques are often used to determine the number of simulation replications required so that a prespecified level of correct selection is guaranteed at a modest possible computational expense. As most real-life systems are multiobjective in nature, in this paper, we consider a multiobjective ranking and selection problem, where the system designs are evaluated in terms of more than one performance measure. We incorporate the concept of Pareto optimality into the ranking and selection scheme, and try to find all of the nondominated designs rather than a single "best" one. A simple sequential solution method is proposed to allocate the simulation replications. Computational results show that the proposed algorithm is efficient in terms of the total number of replications needed to find the Pareto set.
Year
DOI
Venue
2004
10.1109/WSC.2004.1371365
Winter Simulation Conference
Keywords
Field
DocType
multi-objective simulation model,multiobjective ranking problem,simulation replication,budgeting,multiobjective selection problem,best system design,correct selection,multiobjective simulation model,selection problem,pareto optimality,optimal computing budget allocation,selection scheme,pareto optimisation,simulation,multi-objective ranking,sampling methods,selection technique,pareto set,simulation efficiency,system design,simulation model
Mathematical optimization,Ranking,Optimal computing budget allocation,Computer science,Systems design,Simulation modeling,Sampling (statistics),Pareto analysis,Pareto principle
Conference
Volume
ISBN
Citations 
1
0-7803-8786-4
25
PageRank 
References 
Authors
1.36
12
4
Name
Order
Citations
PageRank
Loo Hay Lee1115993.96
Ek Peng Chew245944.07
Suyan Teng31136.92
David Goldsman4904159.71