针对带批量加工设备的柔性铸造作业车间调度问题的混合粒子群优化方法

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2024-10-09 DOI:10.1049/cim2.12117
Wei Zhang, Mengzhen Zhuang, Hongtao Tang, Xinyu Li, Shunsheng Guo
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引用次数: 0

摘要

基于对柔性作业车间调度问题(FJSP)的分析和对消耗性铸造工艺的研究,提出了具有批量加工机器的柔性铸造作业车间调度问题(FCJSP)。考虑到能耗影响下的生产周期,作者结合铸造生产的特点,将时间执行窗口应用到 FCJSP 模型中。开发了一种混合粒子群优化算法(HPSO)来求解 FCJSP。HPSO 采用分块整合解码规则来解决调度整合问题。粒子群优化算法采用离散和连续两种搜索策略进行全局搜索。此外,局部搜索采用基于知识驱动技术的邻域操作塔布搜索。模拟实验证明了所提出的优化模型的可行性。最后,HPSO 算法被成功应用于实际的消耗性铸件调度。结果表明,该算法比之前报道的算法更高效、更稳健。
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A hybrid particle swarm optimisation for flexible casting job shop scheduling problem with batch processing machine

A flexible casting job shop scheduling problem (FCJSP) with batch processing machines is proposed based on the analysis of the flexible job shop scheduling problem (FJSP) and the study of the expendable casting process. Considering the makespan under the influence of the energy consumption, the authors apply the time execution window to the FCJSP model in conjunction with the characteristics of casting production. A hybrid particle swarm optimisation algorithm (HPSO) is developed to solve the FCJSP. The HPSO employs a block integration decoding rule to address scheduling integration. Particle swarm optimisation is used for global search, employing both discrete and continuous search strategies. Furthermore, the local search employs tabu search with neighbourhood operations based on knowledge-driven techniques. Simulation experiments demonstrate the feasibility of the proposed optimisation model. In the end, the HPSO algorithm has been successfully applied to the real expendable casting scheduling. The results demonstrate that it is more efficient and robust than previously reported algorithms.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
期刊最新文献
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