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Minimising material handling cost in cell formation with alternative processing routes by grouping genetic algorithm

机译:通过分组遗传算法,通过其他处理路径将细胞形成中的物料处理成本降至最低

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The alternative processing route is one of the important design factors for the cell formation problem (CFP) in cellular manufacturing systems (CMSs). Genetic algorithm (GA) is a popular method for solving the CFPs, because GA is capable of searching large regions of the solution's space while being less susceptible to getting trapped in local optima. However, the disadvantage of classical GAs is that the number of manufacturing cells should be known in advance. Knowing the actual number of manufacturing cells is relatively difficult before the CMS design is determined. Grouping genetic algorithm (GGA) is capable of solving CFP without predetermination of the number of cells, which is introduced by Falkenauer's GGA (1998). In order to adopt the GGA on CFP with alternative processing routes, we develop a new chromosome representation, a local optimisation algorithm for crossover operator and special mutation operators. These efforts ensure the efficiency of our method and are indicated in the numerical examples, and improved solutions are also obtained in the numerical examples.
机译:替代处理路线是细胞制造系统(CMS)中细胞形成问题(CFP)的重要设计因素之一。遗传算法(GA)是解决CFP的一种流行方法,因为GA能够搜索解决方案空间的较大区域,同时不易陷入局部最优状态。但是,传统GA的缺点是制造单元的数量应事先知道。在确定CMS设计之前,了解制造单元的实际数量相对困难。分组遗传算法(GGA)能够在不预先确定细胞数的情况下求解CFP,这是Falkenauer的GGA(1998)提出的。为了在CFP上采用GGA并采用其他处理路径,我们开发了一种新的染色体表示法,一种针对交叉算子和特殊突变算子的局部优化算法。这些努力确保了我们方法的有效性,并在数值示例中进行了说明,并且在数值示例中也获得了改进的解决方案。

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