在预算不确定的情况下计算最坏情况下的到期日违约情况

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

Operations Research Letters Pub Date : 2024-07-29 DOI:10.1016/j.orl.2024.107148

Igor Malheiros , Artur Pessoa , Michaël Poss , Anand Subramanian

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引用次数: 0

摘要

我们研究的问题是，在考虑总违规次数或迟到作业数量时，如何最大限度地减少对到期日期的违规次数。我们考虑了经典的完成时间和启发式方法中有用的变体。这四个问题出现在（精确或启发式地）解决具有发布日期、到期日期/截止日期和处理时间不确定性的稳健调度问题，以及具有（软）时间窗和旅行时间不确定性的路由问题时。我们为这四个问题提供了多项式动态编程算法。

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Computing the worst-case due dates violations with budget uncertainty

We study the problem of maximizing the violation of due dates when considering either the total violation, or the number of jobs that are tardy. We consider classical completion times and a variant useful in heuristics. The four problems arise when solving (exactly or heuristically) robust scheduling problems with release and due dates/deadlines and processing time uncertainty, and also routing problems with (soft) time windows and travel time uncertainty. We provide polynomial dynamic programming algorithms for the four problems.

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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.