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Flexible Job-Shop Rescheduling for New Job Insertion by Using Discrete Jaya Algorithm

机译:使用离散Jaya算法灵活地重新安排新车间的Job-shop的工作时间

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摘要

Rescheduling is a necessary procedure for a flexible job shop when newly arrived priority jobs must be inserted into an existing schedule. Instability measures the amount of change made to the existing schedule and is an important metrics to evaluate the quality of rescheduling solutions. This paper focuses on a flexible job-shop rescheduling problem (FJRP) for new job insertion. First, it formulates FJRP for new job insertion arising from pump remanufacturing. This paper deals with bi-objective FJRPs to minimize: 1) instability and 2) one of the following indices: a) makespan; b) total flow time; c) machine workload; and d) total machine workload. Next, it discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya and improves it to solve FJRP. Two simple heuristics are employed to initialize high-quality solutions. Finally, it proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya. Finally, it performs experiments on seven real-life cases with different scales from pump remanufacturing and compares DJaya with some state-of-the-art algorithms. The results show that DJaya is effective and efficient for solving the concerned FJRPs.
机译:当必须将新到达的优先级作业插入到现有的计划中时,重新计划是灵活工作车间的必要步骤。不稳定度衡量对现有计划进行的更改量,并且是评估重新计划解决方案质量的重要指标。本文重点介绍了用于新职位插入的灵活的Job-shop重新计划问题(FJRP)。首先,它为由泵再制造引起的新工作插入制定FJRP。本文讨论了双目标FJRP,以最大程度地减少:1)不稳定和2)以下指标之一: b)总流动时间; c)机器工作量; d)机器总工作量。接下来,它离散化了一种名为Jaya的新颖简单的元启发式方法,生成了DJaya并对其进行了改进以解决FJRP。使用两种简单的启发式方法初始化高质量的解决方案。最后,它提出了五个面向目标的本地搜索运算符及其四个集合,以提高DJaya的性能。最后,它对来自泵再制造的不同规模的七个实际案例进行了实验,并将DJaya与一些最新算法进行了比较。结果表明,DJaya是解决相关FJRP的有效方法。

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