Scheduling identical parallel machines involving flexible maintenance activities

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications Pub Date : 2024-11-14 DOI:10.1016/j.eswa.2024.125722

Chunhao Li , Feng Wang , Jatinder N.D. Gupta , Tsui-Ping Chung

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Abstract

Motivated by a practical situation in chip manufacturing process, for the first time in the literature, this paper considers an identical parallel-machine scheduling problem with new flexible maintenance activities to minimize makespan where a maintenance activity is needed if and only if the machine capability has deteriorated by a critical value. To address the proposed problem, a mixed integer linear programming model and a lower bound are established. Since this problem is NP-hard, a combined constructive heuristic algorithm with six priority rules is developed. In order to improve the solution obtained by the proposed combined heuristic algorithm, an embedded learning mechanism is combined with the existing artificial immune system (AIS) algorithm to help self-adjust and modify the search direction. The effectiveness of the proposed combined constructive heuristic and the AIS algorithms is empirically tested on the randomly generated problem instances. These computational results show that the proposed AIS algorithm can generate better near-optimal solutions than several adaptations of the existing algorithms.

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对涉及灵活维护活动的相同并联机器进行调度

受芯片制造过程中实际情况的启发，本文在文献中首次考虑了一个相同的并行机器调度问题，该问题具有新的灵活维护活动，以最大限度地缩短生产周期，其中只有当机器能力下降到临界值时才需要进行维护活动。为了解决所提出的问题，本文建立了一个混合整数线性规划模型和一个下限。由于该问题具有 NP 难度，因此开发了一种包含六条优先规则的组合式构造启发式算法。为了改进所提出的组合启发式算法所得到的解，将嵌入式学习机制与现有的人工免疫系统（AIS）算法相结合，以帮助自我调整和修改搜索方向。在随机生成的问题实例上，对所提出的组合式构造启发式算法和 AIS 算法的有效性进行了实证测试。计算结果表明，所提出的 AIS 算法能比现有算法的几种适应性算法生成更好的近优解决方案。

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

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.