Title: Assignment of machines in cell layout using modified artificial bee colony algorithm

Authors: A. Adinarayanan; M. Uthayakumar; G. Prabhakaran; V. Anbumalar

Addresses: Department of Mechanical Engineering, Anand Institute of Higher Technology, Chennai – 603103, India ' Department of Mechanical Engineering, Kalasalingam University, Krishnankoil – 626126, India ' Department of Mechanical Engineering, Velammal Engineering College, Chennai – 600066, India ' Department of Mechanical Engineering, Velammal College of Engineering and Technology, Madurai – 625009, India

Abstract: Cellular manufacturing (CM) is now an established international practice to integrate: equipment, people, and systems into 'focused factories', 'mini-businesses' or 'cells' with clear customers, responsibilities and boundaries. The major element in exploiting the benefits of CM is efficient layout designs. In this paper, problem of machine assignment is considered in a cellular layout. The focus is on the intra-cell machines allocation, because the inter-cell material flows should be minimal in CM. The modified artificial bee colony (MABC) algorithm is proposed to solve the layout problem. The algorithm tackles one cell at a time with a maximum of nine machines, which will be assigned into a 3 × 3 grid space according to the ranking result. This problem involves five parts and five quantitative demand periods are considered. To examine the performance of the proposed MABC, datasets are taken from the literature and the results are compared with those of other approaches. The proposed MABC provides an optimal layout exactly.

Keywords: modified ABC; artificial bee colony; MABC; machine assignment; intra-cell machine allocation; cellular manufacturing; manufacturing cells; cell layout; material flow; optimisation; metaheuristics; swarm intelligence.

DOI: 10.1504/IJCAET.2017.083390

International Journal of Computer Aided Engineering and Technology, 2017 Vol.9 No.2, pp.166 - 178

Received: 06 Aug 2014
Accepted: 22 Sep 2014
Published online: 27 Mar 2017 *

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