Title: Cell loading and shipment optimisation in a cellular manufacturing system: an integrated genetic algorithms and neural network approach
Authors: Gokhan Egilmez; Can Celikbilek; Melih Altun; Gürsel A. Süer
Addresses: North Dakota State University, 1410 14th Avenue North, Room 202 Civil & Industrial Engineering, Fargo, North Dakota 58102, USA ' Industrial and Systems Engineering Department, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA ' Maxim Integrated, 160 Rio Robles, San Jose, CA 95134, USA ' Industrial and Systems Engineering Department, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA
Abstract: In this paper, cell loading and shipment method optimisation problem in a cellular manufacturing system are studied. A hierarchical methodology that consists of mathematical optimisation model, genetic algorithms (GAs) and artificial neural networks (ANNs) were proposed. The mathematical model is compared with the GA in terms of the optimisation performance. Next, ANN model was developed as decision support tool to study the impact of GA parameters on the solution quality. Several problem sizes were experimented with the proposed GA and the mathematical model, and compared. GA was run to make a total of 648 sample solutions for the 20-job problem. Next, ANN model was built based on the sample solutions' data and the optimal ANN model is identified out of 1,000 networks. The results were also coupled with sensitivity and statistical analyses, which indicated that type of crossover and mutation operators, had the greatest impact on the solution quality.
Keywords: cell loading; shipping; genetic algorithms; artificial neural networks; ANNs; shipment optimisation; cellular manufacturing systems; CMS; manufacturing cells; mathematical modelling.
International Journal of Industrial and Systems Engineering, 2016 Vol.24 No.3, pp.302 - 332
Received: 12 Sep 2014
Accepted: 11 Jan 2015
Published online: 22 Sep 2016 *