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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Flexible flowline scheduling problem with constraints for the beginning and terminating time of processing of jobs at stages
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Flexible flowline scheduling problem with constraints for the beginning and terminating time of processing of jobs at stages

机译：灵活的流水线调度问题，在阶段中限制了作业处理的开始和终止时间

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摘要

In this research, the flexible flow line scheduling problem with minimisation of makespan as the objective by considering constraints for the beginning and terminating times of processing the jobs at stages is investigated for the first time. The process of jobs at some stages cannot be started before a specific time and should be completed before another specific time. Since the process of jobs at each stage should be performed at an interval time, in spite of regular scheduling problems, every schedule cannot be considered as a feasible solution. A mathematical model is developed to solve the proposed research problem optimally. Since the research problem is shown to be NP-hard, several hybrid metaheuristic algorithms based on particle swarm optimisation (PSO) and simulated annealing (SA) are proposed to heuristically solve large-size problems. In these algorithms, for each renewed particle in PSO algorithm, a local search is performed based on SA. The major difference among the proposed algorithms is the rules used to perform the local search. The performances of the proposed algorithms are compared based on randomly generated test problems. Based on the results of this comparison, the best proposed metaheuristic algorithm has a good performance with the average percentage gap of 0.289% compared to the optimal solution for the test problems that can be solved optimally.
机译：在这项研究中，首次研究了以最小化制造时间为目标的柔性流水线调度问题，该问题考虑了阶段性处理作业的开始和终止时间的约束。某些阶段的作业过程无法在特定时间之前启动，而应在另一个特定时间之前完成。由于每个阶段的作业处理都应间隔一定时间执行，因此尽管存在定期的计划问题，但不能将每个计划视为可行的解决方案。建立了数学模型以最佳地解决所提出的研究问题。由于研究问题被证明是NP难的，因此提出了几种基于粒子群优化（PSO）和模拟退火（SA）的混合元启发式算法来启发式地解决大型问题。在这些算法中，对于PSO算法中的每个更新粒子，都基于SA进行局部搜索。所提出的算法之间的主要区别是用于执行本地搜索的规则。基于随机生成的测试问题，比较了所提出算法的性能。根据此比较的结果，与可以最佳解决的测试问题的最佳解决方案相比，最佳提出的元启发式算法具有良好的性能，平均百分比差距为0.289％。

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