A worst-case formulation for constrained ranking and selection with input uncertainty

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)648-662
Journal / PublicationNaval Research Logistics
Volume66
Issue number8
Online published24 Oct 2019
Publication statusPublished - Dec 2019

Abstract

In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst-case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.

Research Area(s)

  • input uncertainty, OCBA, ranking and selection, stochastic constraints, simulation optimization, SIMULATION BUDGET ALLOCATION, DISTRIBUTIONALLY ROBUST OPTIMIZATION, SEQUENTIAL-PROCEDURES, FRAMEWORK, 2-STAGE, DESIGN

Citation Format(s)

A worst-case formulation for constrained ranking and selection with input uncertainty. / Shi, Zhongshun; Gao, Siyang; Xiao, Hui et al.
In: Naval Research Logistics, Vol. 66, No. 8, 12.2019, p. 648-662.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review