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利用重复观测为主观约束寻找可行系统

Finding Feasible Systems for Subjective Constraints Using Recycled Observations

INFORMS journal on computing · 2022
被引 7
人大 BUTD24ABS 3

中文导读

研究了在随机约束下从有限个仿真系统中找出可行或近似可行系统的问题,提出一种回收观测数据的无差异区方法,在多个阈值下保证正确决策概率,减少所需观测次数。

Abstract

We consider the problem of finding a set of feasible or near-feasible systems among a finite number of simulated systems in the presence of stochastic constraints. When the constraints are subjective, a decision maker may want to test multiple threshold values for the constraints. Or the decision maker may simply want to determine how a set of feasible systems changes as constraints become more strict with the objective of pruning systems or finding the system with the best performance. When only the constraint thresholds change for the same set of underlying systems, it is natural to reuse observations collected from the feasibility check with a different threshold value. We present an indifference-zone procedure that recycles observations and provide an overall probability of correct decision for all threshold values. Our numerical experiments show that the proposed procedure performs well in reducing the required number of observations while providing a statistical guarantee on the probability of correct decision. Summary of Contribution: We consider the problem of determining the feasibility of a finite number of systems in the presence of subjective constraints on performance measures that can be estimated only by stochastic simulation. Specifically, our work focuses on the situation where the decision maker is willing to relax some constraint thresholds if necessary to achieve feasibility. Also, we discuss how our proposed procedures can help select the best system in the presence of multiple objectives. This is the first work that considers subjective constraints in the field of ranking and selection in simulation and it provides a practically useful decision-making tool with more flexibility on feasibility determination. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.1227 .

运筹学仿真优化排序与选择随机约束