Extended Local Clustering Organization using Rule-based Neighborhood Search for Job-shop Scheduling Problem
Extended Local Clustering Organization using Rule-based Neighborhood Search for Job-shop Scheduling Problem
复制标题
使用基于规则的邻域搜索扩展本地聚类组织来解决车间调度问题
DOI：
10.1007/978-3-319-13356-0_37
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Masahito Yamamoto
中科院分区：
文献类型：
--
作者：
Yasumasa Tamura;Hiroyuki Iizuka;Masahito Yamamoto
作业车间调度问题（JSP）是最难的组合优化问题之一。局部聚类组织（LCO）是由 Furukawa 等人提出的。解决诸如元启发式算法之类的组合优化问题。通过与遗传算法的比较，验证了其对于JSP的有效性。然而，由于 LCO 基于贪婪搜索，因此解决方案常常陷入局部极小值。为了改善这个问题，本研究提出了一种使用优先级规则的新颖的邻域搜索方法。本文还展示了与搜索方法相结合的扩展LCO。
Job-shop scheduling problem(JSP) is one of the hardest combinatorial optimization problems. Local clustering organization (LCO) is proposed by Furukawa et al. to solve such a combinatorial optimization problems as the metaheuristic algorithm. Its effectiveness for the JSP is verified by the comparison with genetic algorithm. However, since LCO is based on the greedy search, the solution is often trapped in local minima. To improve the problem, this study proposes a novel neighborhood search method using priority rules. This paper also shows the extended LCO integrated with the search method.