Tabu search for a parallel-machine scheduling problem with periodic maintenance, job rejection and weighted sum of completion times.

IF 1.4 4区 工程技术 Q4 ENGINEERING, MANUFACTURING
Journal of Scheduling Pub Date : 2022-01-01 Epub Date: 2021-10-17 DOI:10.1007/s10951-021-00711-9
Hanane Krim, Nicolas Zufferey, Jean-Yves Potvin, Rachid Benmansour, David Duvivier
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引用次数: 4

Abstract

We consider in this work a bicriteria scheduling problem on two different parallel machines with a periodic preventive maintenance policy. The two objectives considered involve minimization of job rejection costs and weighted sum of completion times. They are handled through a lexicographic approach, due to a natural hierarchy among the two objectives in the applications considered. The main contributions of this paper are first to present a new problem relevant to practice, second, to develop a mixed-integer-linear-program model for the problem, and third, to introduce two generalizable tabu-search metaheuristics relying on different neighborhood structures and solution spaces. Computational results for 120 instances (generated from a real case) are reported to empirically demonstrate the effectiveness of the proposed metaheuristics.

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具有周期性维护、作业拒绝和加权完成时间和的并行机器调度问题的禁忌搜索。
本文研究了具有定期预防性维护策略的两台不同并行机器的双准则调度问题。考虑的两个目标包括最小化工作拒绝成本和加权完成时间总和。由于所考虑的应用程序中的两个目标之间存在自然的层次结构,因此可以通过字典方法处理它们。本文的主要贡献在于:首先,提出了一个与实践相关的新问题;其次,为该问题建立了一个混合整数-线性规划模型;第三,引入了依赖于不同邻域结构和解空间的两种可推广的禁忌搜索元启发式方法。计算结果为120个实例(从一个真实的情况下产生)报告,以经验证明所提出的元启发式的有效性。
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
Journal of Scheduling
Journal of Scheduling 工程技术-工程:制造
CiteScore
3.80
自引率
10.00%
发文量
49
审稿时长
6-12 weeks
期刊介绍: The Journal of Scheduling provides a recognized global forum for the publication of all forms of scheduling research. First published in June 1998, Journal of Scheduling covers advances in scheduling research, such as the latest techniques, applications, theoretical issues and novel approaches to problems. The journal is of direct relevance to the areas of Computer Science, Discrete Mathematics, Operational Research, Engineering, Management, Artificial Intelligence, Construction, Distribution, Manufacturing, Transport, Aerospace and Retail and Service Industries. These disciplines face complex scheduling needs and all stand to gain from advances in scheduling technology and understanding.
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