The emergence of fuzzy sets makes job-shop scheduling problem (JSSP) become better aligned with the reality. This article addresses the JSSP with fuzzy execution time and fuzzy completion time (FJSSP). We choose the classic differential evolution (DE) algorithm as the basic optimization framework. The advantage of the DE algorithm is that it uses a special evolutionary strategy of difference vector sets to carry out mutation operation. However, DE is not very effective in solving some instances of FJSSP. Therefore, we propose a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results. The proposed selection operator adopted in this article aims at a temporary retention of all children generated by the parent generation, and then selecting N better solutions as the new individuals from N parents and N children. Various examples of fuzzy shop scheduling problems are experimented with to test the performance of the improved DE algorithm. The NSODE algorithm is compared with a variety of existing algorithms such as ant colony optimization, particle swarm optimization, and cuckoo search. Experimental results show that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature.

中文翻译：

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用选择机制改进的DE算法解决模糊作业车间调度问题

模糊集的出现使得作业车间调度问题（JSSP）更加符合现实。本文讨论具有模糊执行时间和模糊完成时间 (FJSSP) 的 JSSP。我们选择经典的差分进化（DE）算法作为基本的优化框架。DE算法的优点在于它采用了特殊的差分向量集进化策略来进行变异操作。但是，DE 在解决 FJSSP 的某些实例方面不是很有效。因此，我们提出了一种新的选择机制来增强通用 DE 算法（NSODE）以获得更好的优化结果。本文采用的建议选择算子旨在临时保留父代生成的所有子代，然后从N个父母和N个孩子中选择N个更好的解决方案作为新个体。对模糊车间调度问题的各种示例进行了试验，以测试改进的 DE 算法的性能。NSODE算法与蚁群优化、粒子群优化、布谷鸟搜索等多种现有算法进行了比较。实验结果表明，与文献报道的几种算法产生的解决方案相比，NSODE 可以获得更好的可行解决方案。