Piecewise affine decision rules for contextual chance-constrained stochastic programming

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2025-04-09 DOI:10.1016/j.orl.2025.107296

Junhao Qin , Ziyi Zou , Liu Liu , Yongchao Liu

引用次数: 0

Abstract

This paper proposes a contextual chance-constrained programming model (CCCP), where a measurable function from the feature space to the decision space is to be optimized under the chance constraint. We present a tractable approximation of CCCP by the piecewise affine decision rule (PADR) method. We quantify the approximation results from two aspects: the gap of optimal values and the feasibility of the approximate solutions. Finally, numerical tests are conducted to verify the effectiveness of the proposed methods.

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.