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A decomposition-based approach to flexible flow shop scheduling under machine breakdown

机译：机器故障下基于分解的柔性流水车间调度方法

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Manufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling under machine breakdown.
机译：实际生产中的制造系统通常是动态的，并且经常受到各种不确定性的影响。近年来，不确定性下的生产调度研究引起了广泛的关注。尽管已开发出一些方法来解决此问题，但不确定性下的调度本质上仍然难以通过任何一种方法来解决。本文考虑了机器故障情况下柔性流水车间（FFS）调度问题的makepan优化。提出了一种基于分解的新颖方法，将FFS调度问题分解为几个群集调度问题，可以通过不同的方法更轻松地解决。基于提出的机器分配算法和加权集群有效性指标，开发了一种相邻的K均值聚类算法，以首先将FFS的机器分组为适当数量的机器集群。然后有选择地采用两个最佳反向传播网络，分别对应于同时和非同时到达作业的情况，以为每个机器集群分配最短处理时间（SPT）或遗传算法（GA），以解决集群调度问题。如果使用相同的方法分配了两个相邻的机器集群，则它们随后将合并。在机器分组和方法分配之后，通过将解决方案集成到子问题中来生成总体计划。计算结果表明，对于机器故障下的FFS调度，该方法优于单独的SPT和GA。

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