A metaheuristic approach for a cubic cell formation problem


BURUK ŞAHİN Y., ALPAY Ş.

EXPERT SYSTEMS WITH APPLICATIONS, vol.65, pp.40-51, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 65
  • Publication Date: 2016
  • Doi Number: 10.1016/j.eswa.2016.08.034
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.40-51
  • Keywords: Cellular manufacturing system, Part-machine-worker cell formation, Genetic algorithm, Mathematical model, GENETIC-ALGORITHM, MATHEMATICAL-MODEL, MANUFACTURING SYSTEM, GROUP-TECHNOLOGY, OPTIMIZATION ALGORITHM, OPERATOR ASSIGNMENT, WORKER ASSIGNMENT, MACHINE, DESIGN, ALLOCATION
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

The minimizations of voids and exceptional elements by considering the part-machine incidence matrix of the cell formation problem have been discussed in the literature. In recent years, a few mathematical models considering the assignment of workers to cells in addition to part and machine have been proposed to fully utilize manufacturing systems. Although the proposed mathematical models can produce the best solutions for small sized problems within reasonable times, they are inadequate to produce the best solutions for large sized real life cases due to the NP-hard nature of the problem. In this study, a genetic algorithm has been proposed for the problem of part-machine-worker cell formation. Furthermore, the Taguchi method, as a statistical optimization technique, has been used to determine the appropriate levels of the parameters. The performance of the proposed genetic algorithm has been tested using test data from the literature for small sized problems and using data that was generated in this study for large sized problems. The experimental results of this study show that the proposed genetic algorithm can produce the optimal solutions for small sized problems and that the proposed algorithm can yield optimal or near-optimal solutions for large sized problems within reasonable times. (C) 2016 Elsevier Ltd. All rights reserved.