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A worst‐case formulation for constrained ranking and selection with input uncertainty
Author(s) -
Shi Zhongshun,
Gao Siyang,
Xiao Hui,
Chen Weiwei
Publication year - 2019
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.21871
Subject(s) - mathematical optimization , ranking (information retrieval) , selection (genetic algorithm) , budget constraint , computer science , constraint (computer aided design) , focus (optics) , measure (data warehouse) , mathematics , data mining , physics , geometry , neoclassical economics , artificial intelligence , machine learning , optics , economics
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.