Approximate Simulation Budget Allocation for Selecting the Best Design in the Presence of Stochastic Constraints

Author

Loo Hay Lee ; Pujowidianto, Nugroho A. ; Ling-Wei Li ; Chen, Ci ; Yap, C.M.

Author_Institution

Ind. & Syst. Eng. Dept., Nat. Univ. of Singapore, Singapore, Singapore

Volume

57

Issue

11

fYear

2012

Firstpage

2940

Lastpage

2945

Abstract

We develop a new Optimal Computing Budget Allocation (OCBA) approach for the ranking and selection problem with stochastic constraints. The goal is to maximize the probability of correctly selecting the best feasible design within a fixed simulation budget. Based on some approximations, we derive an asymptotic closed-form allocation rule which is easy to compute and implement and can help provide more insights about the allocation. The numerical testing shows that our approach can enhance the simulation efficiency.

Keywords

budgeting; probability; resource allocation; simulation; stochastic processes; OCBA approach; approximate simulation budget allocation; asymptotic closed-form allocation rule; design selection; fixed simulation budget; numerical testing; optimal computing budget allocation approach; probability; ranking and selection problem; simulation efficiency; stochastic constraints; Approximation methods; Computational modeling; Nickel; Numerical models; Optimization; Resource management; Constrained optimization; optimal computing budget allocation; ranking and selection; simulation;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.2012.2195931

Filename

6189041