An improved genetic algorithm for the machine-part cell formation problem
An improved genetic algorithm for the machine-part cell formation problem
复制标题
机器零件单元形成问题的改进遗传算法
DOI：
10.1007/s13198-021-01615-9
复制
发表时间：
2022
影响因子：
2
通讯作者：
Manash Hazarika
中科院分区：
文献类型：
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作者：
Manash Hazarika
单元化制造系统(CMS)处理的是公司使用的一套程序，通过解决工厂中遇到的技术和物流问题，并确保产品符合质量标准，来驱动生产设施和有效地管理生产。CMS被认为是一种高效的生产方法，使批量制造尽可能高效和多产。CMS依靠成组技术(GT)的理论，将不同的机器分组到机床单元中，并将零件分组到零件族中，以利用它们在设计和生产过程中的相似性。它减少了细胞间的总传递，并最大限度地利用了机器细胞内的操作数量。提出了一种元启发式遗传算法来解决CMS中的机器单元形成问题(CFP)，并注重通过减少对角区块中的外部元素和空洞元素来最大化分组效率(GC)。对文献中的36个标准问题进行了计算。结果证实，所提出的元启发式在GC方面已经被证明能够产生解决方案，与其他可访问的算法相比，要么是增强的，要么是积极的。
Cellular manufacturing system (CMS) deals with the set of procedures used by the company to drive the production facilities and to manage production efficiently by solving the technical and logistic problems encountered in the factory, and ensuring that products meet quality standards. The CMS is well thought-out as an efficient production approach to make batch manufacturing as efficient and productive as possible. The CMS relies on the theory of group technology (GT) for grouping dissimilar machines in machine cells and grouping parts into part families to take the benefit of their similarities in design and production process. It reduce total intercellular pass as well as to make the most of the number of operations within a machine cell. This paper presents a meta-heuristic genetic algorithm to resolve machine cell formation problem (CFP) in CMS and paying concentration on maximizing grouping efficacy (GC) by reducing outside elements and void elements in diagonal blocks. Computational work was carried out on 36 standard problems from the literature. The outcome confirms that the proposed meta-heuristic in terms of GC has shown to produce solutions are either enhanced or aggressive with other accessible algorithms.