Abstract
In manufacturing-cell-formation research, a major concern is to make groups of machines into machine cells and parts into part families. Extensive work has been carried out in this area using various models and techniques. Regarding these ideas, in this paper, experiments with varying parameters of the popular metaheuristic algorithm known as the genetic algorithm have been carried out with a bi-criteria objective function: the minimization of intercell moves and cell load variation. The probability of crossover (A), probability of mutation (B), and balance weight factor (C) are considered parameters for this study. The data sets used in this paper are taken from benchmarked literature in this field. The results are promising regarding determining the optimal combination of the genetic parameters for the machine-cell-formation problems considered in this study.
Highlights
Facility-layout optimization problems are nonlinear, nonconvex, and multimodal in their nature
The presented work attempts to employ the Taguchi approach to find an optimal combination of parameters that impact the efficiency of the genetic algorithm and to explore whether the optimal combination of the genetic operators for the given type of manufacturing-cell-formation problem (MCFP) can be influenced by the magnitude of the noise factors, which is represented by matrix size in our case
It is worth mentioning other works that directly relate to the manufacturing cellformation problem, such as the hybrid genetic algorithm (GA)/branch and bound approach to solve the manufacturing-cell-formation problem using a graph partitioning formulation, which was proposed by Boulif and Atif [37]
Summary
Facility-layout optimization problems are nonlinear, nonconvex, and multimodal in their nature. Facility-layout problems (FLP) can be divided according to types of manufacturing systems into four basic categories, which are product layout, process layout, static layout, and cellular layout [1]. Taking this classification into account, the proposed study addresses the cellular manufacturing problem. The presented work attempts to employ the Taguchi approach to find an optimal combination of parameters that impact the efficiency of the genetic algorithm and to explore whether the optimal combination of the genetic operators for the given type of MCFP can be influenced by the magnitude of the noise factors, which is represented by matrix size in our case
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