A hybrid genetic algorithm for machine-part grouping |
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| Authors: | Adnan Tariq Iftikhar Hussain Abdul Ghafoor |
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| Affiliation: | 1. Department of Mechanical Engineering, College of Electrical & Mechanical Engineering, National University of Science & Technology, Peshawar Road, Rawalpindi 4600, Pakistan;2. Department of Industrial Engineering, NWFP, University of Engineering and Technology, Peshawar, Pakistan;1. Laboratoire de Recherche en Automatique, Ecole Nationale d’Ingénieurs de Tunis, BP 37, le Belvédère, 1002 Tunis, Tunisia;2. Evaluation and Safety of Automated Transport Systems, French Institute of Science and Technology for Transport, Development and Networks, 20 rue Elisée RECLUS BP 70317, F-59651 Villeneuve d’Ascq, France;3. Laboratoire d’Automatique, Génie Informatique et Signal Ecole Centrale de Lille, Cité Scientifique BP 48, 59651 Villeneuve d’Ascq, France;1. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran;2. Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., 15916-34311 Tehran, Iran |
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| Abstract: | Cellular manufacturing (CM), which incorporates the flexibility of job shops and high production rate of flow lines, has been seen as a promising alternative for batch type production. Although CM provides great benefits, the design of CM is quite complex for real world problems. The main problem in the design of a CM is the formation of machine groups and corresponding part families. This paper aims at developing an approach that combines a local search heuristic (LSH) with genetic algorithm (GA). The approach has been tested on a number of problems from the literature. The results show that new approach not only converges to the best solution in the earlier generations but also produces solutions that are as accurate as any of the results reported, so far, in literature. Also, in certain cases the results produced by this technique are even better than the previously reported results. |
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