IEEE Trans Cybern. 2019 May;49(5):1944-1955. doi: 10.1109/TCYB.2018.2817240. Epub 2018 Apr 24.
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.
调度重排是灵活作业车间在有新的优先级任务需要插入到现有计划时的必要程序。不稳定性衡量了对现有计划进行更改的程度，是评估重排解决方案质量的重要指标。本文主要研究了新任务插入时的灵活作业车间调度重排问题(FJRP)。首先，它为源于泵再制造的新任务插入问题(FJRP)进行了建模。本文处理了双目标的 FJRPs，以最小化：1)不稳定性和 2)以下指标之一：a)最大完工时间；b)总流程时间；c)机器工作量；和 d)总机器工作量。接下来，它对一种新颖而简单的元启发式算法 Jaya 进行了离散化，得到了 DJaya，并对其进行了改进以解决 FJRP。采用了两种简单的启发式算法来初始化高质量的解决方案。最后，提出了五个面向目标的局部搜索算子，并将它们组合成四个集合，以提高 DJaya 的性能。最后，在来自泵再制造的七个不同规模的真实案例上进行了实验，并将 DJaya 与一些最先进的算法进行了比较。结果表明，DJaya 对于解决所关注的 FJRPs 是有效和高效的。
