Uncertainty reduction in robust optimization

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

Operations Research Letters Pub Date : 2024-07-01 DOI:10.1016/j.orl.2024.107131

Ayşe N. Arslan , Michael Poss

引用次数: 0

Abstract

Uncertainty reduction has recently been introduced in the robust optimization literature as a relevant special case of decision-dependent uncertainty. Herein, we identify two relevant situations in which the problem is polynomially solvable. We further provide insights into possible MILP reformulations and the strength of their continuous relaxations.

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减少稳健优化中的不确定性

最近，稳健优化文献将不确定性降低作为决策相关不确定性的一个相关特例提出来。在此，我们确定了该问题可多项式求解的两种相关情况。我们进一步深入探讨了可能的 MILP 重构及其连续松弛的强度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.