Title: A genetic algorithm-based grouping method for a cell formation problem with the efficacy measure
Authors: Mojtaba Salehi, Reza Tavakkoli-Moghaddam
Addresses: Department of Industrial Engineering, University of Bojnourd, Bojnourd, Khorasane Shomali, Iran. ' Department of Industrial Engineering, College of Engineering, University of Tehran, Enghelab, Amir Abad, Tehran, Iran
Abstract: Over the past 25 years, the machine–part cell formation problem (CFP) has been the subject of numerous studies. The CFP consists of constructing a set of machine cells and their corresponding product families with the objective of minimising the inter-cell movement of parts while maximising the machine utilisation. This article presents a grouping genetic algorithm for the CFP that uses the grouping efficacy measure. We solve the CFP without pre-determining the number of cells. We also make some effort to improve the efficiency of our algorithm with respect to initialisation of the population, keeping a crossover operator from cloning. The computational results using the grouping efficacy measure for a set of CFPs from the literature are presented. The proposed algorithm performs well on all the test problems, exceeding or matching the solution quality of the results presented in the previous literature for most problems.
Keywords: cell formation; cellular manufacturing; manufacturing cells; machine utilisation; genetic algorithms; inter-cellular movements; efficacy measures; machine cells; product families; grouping technologies; cell numbers; population initialisation; crossover operators; cloning; grouping efficiency.
International Journal of Industrial and Systems Engineering, 2010 Vol.6 No.3, pp.340 - 359
Published online: 01 Sep 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article