A hybrid genetic algorithm-TOPSIS-computer simulation approach for optimum operator assignment in cellular manufacturing systems

This article presents a decision-making approach based on a hybrid genetic algorithm (GA) and a technique for order performance by similarity to ideal solution (TOPSIS) simulation (HGTS) for determining the most efficient number of operators and the efficient measurement of operator assignment in cellular manufacturing systems (CMS). The objective is to determine the labor assignment in a CMS environment with the optimum performance. We use HGTS for getting near optimum ranking of the alternative with best fit to the fitness function. Also, this approach is performed by employing the number of operators, average lead time of demand, average waiting time of demand, number of completed parts, operator utilization, and average machine utilization as attributes. Also, the entropy method is used to determine the weight of attributes. Furthermore, values of attributes are procured by means of computer simulation. The unique feature of this model is demonstration of efficient ranks of alternatives by reducing the distance between neighborhood alternatives. The superiority and advantages of the proposed HGTS are shown through qualitative and qualitative comparisons with TOPSIS, data envelopment analysis (DEA), and principal component analysis (PCA).

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