Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns
Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns
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DOI：
10.1080/00207543.2010.495088
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发表时间：
2011-08
影响因子：
9.2
通讯作者：
S. Hasan;R. Sarker;D. Essam
中科院分区：
文献类型：
--
作者：
S. Hasan;R. Sarker;D. Essam
车间作业调度问题被认为是最复杂的组合优化问题之一。在我们以前的尝试中，我们混合了遗传算法（GA）与局部搜索技术来解决JSSP。在这项研究中，我们提出了一种改进的局部搜索技术，移位间隙减少（SGR），提高了性能的遗传算法在解决相对困难的测试问题。我们还修改了新的算法JSSP与机器不可用和故障。我们考虑两种情况下的机器不可用。首先，不可用性信息是预先可用的（预测性的），其次，在真实的故障之后信息是已知的（反应性的）。我们表明，修改后的时间表是大多能够恢复，如果中断发生在早期阶段的时间表。
The job-shop scheduling problem (JSSP) is considered to be one of the most complex combinatorial optimisation problems. In our previous attempt, we hybridised a Genetic Algorithm (GA) with a local search technique to solve JSSPs. In this research, we propose an improved local search technique, Shifted Gap-Reduction (SGR), which improves the performance of GAs when solving relatively difficult test problems. We also modify the new algorithm for JSSPs with machine unavailability and breakdowns. We consider two scenarios of machine unavailability. First, where the unavailability information is available in advance (predictive) and, secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules.