A hybrid grouping genetic algorithm for the cell formation problem

  • Authors:

  • Tabitha L. James;Evelyn C. Brown;Kellie B. Keeling

  • Affiliations:

  • Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA;Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA;Department of Business Information Technology, Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2007

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Abstract

The machine-part cell formation problem consists of constructing a set of machine cells and their corresponding product families with the objective of minimizing the inter-cell movement of the products while maximizing machine utilization. This paper presents a hybrid grouping genetic algorithm for the cell formation problem that combines a local search with a standard grouping genetic algorithm to form machine-part cells. Computational results using the grouping efficacy measure for a set of cell formation problems from the literature are presented. The hybrid grouping genetic algorithm is shown to outperform the standard grouping genetic algorithm by exceeding the solution quality on all test problems and by reducing the variability among the solutions found. The algorithm developed performs well on all test problems, exceeding or matching the solution quality of the results presented in previous literature for most problems.