A genetic algorithm with proper parameters for manufacturing cell formation problems

Abstract

One fundamental problem in cellular manufacturing is the formation of product families and machine cells. Many solution methods have been developed for the cell formation problem. Since efficient grouping is the prerequisite of a successful Cellular Manufacturing installation the research in this area will likely be continued. In this paper, we consider the problem of cell formation in cellular manufacturing systems with the objective of maximizing the grouping efficacy. We propose a Genetic Algorithm (GA) to obtain machine-cells and part-families. Developed GA has three different selection and crossover operators. The proper operators and parameters of the GA were determined by design of experiments. A set of 15 test problems with various sizes drawn from the literature is used to test the performance of the proposed algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed GA improves the grouping efficacy for 40% of the test problems.

Hybrid Genetic Algorithm for Machine-Component Cell Formation

“…Banerjee and Das [5] investigated the cell formation problem by Predator-Prey Genetic Algorithm by local selection strategy. Sarac and Ozcelik [9] introduced three different selection and crossover operations and tested the performance of proposed algorithm with an existing algorithm using 15 test problems. Vin and Delchambre [37] defined the generalized cell formation problem and developed a grouping genetic algorithm to solve it.…”

“…Several methods have been developed to solve this cell formation problem, like mathematical programming approach (Mahdavi et al [1], Khaksar-Haghani et al [2], Arkat et al [3]), meta-heuristic approach includes Genetic Algorithm (GA) (Saeedi et al [4], Banerjee and Das [5], Arkat et al [6], Yin and Khoo [7], Ozcelik and Sarac [8], Sarac and Ozcelik [9]), Simulated Annealing (SA) (Wu et al [10], Lin et al [11], Paydar et al [12], Kia et al [13]) and Hybrid heuristics (Rezaeian et al [14], Ghezavati and Saidi-Mehrabad [15], Elbenani and Ferland [16], Rafiei and Ghodsi [17], Paydar and Saidi-Mehrabad [18], Dalfard [19]). Nevertheless, the complexity of the problem and the significant issues involved in obtaining the result create necessity for more effective algorithms.…”

A hybrid simulated annealing for scheduling in dual-resource cellular manufacturing system considering worker movement

“…Arkat et al [2] proposed a branch and bound algorithm to minimize the total number of movements between each pair of machines locating in two different cells. Saraç and Ozcelik [3] used a genetic algorithm to maximize the grouping efficacy. Chung et al [4] proposed an efficient tabu search algorithm to solve the cell formation problem with alternative routings and machine reliability considerations.…”

Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm

“…Prabhakaran [16] has discussed the cell load variation and intercellular moves in cell formation problem using genetic algorithm (GA). Sarac and Ozcelik [17] formulated a genetic algorithm with proper parameters for manufacturing cell formation problems. In their paper, the cell formation in cellular manufacturing systems is considered with the objective of maximizing the grouping efficacy.…”