Formation of general GT cells: an operation-based approach |
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| Affiliation: | 1. Department of Industrial Technology, Indiana State University, Terre Haute, IN 47809 U.S.A.;2. Systems and Industrial Engineering Department, The University of Arizona, Tucson, AZ 85721, U.S.A.;1. Laboratory of Toxicology and Environmental Health, UR11ES70, Sciences Faculty of Sfax, University of Sfax, BP1171, 3000, Sfax, Tunisia;2. Laboratory of Biochemistry, Molecular Mechanisms and Diseases Research Unit, UR12ES08, Faculty of Medicine, University of Monastir, BP5019, 5000, Monsatir, Tunisia;3. Molecular Biology Laboratory, Faculty of Nature and Life Sciences, University of Jijel, PB 98, Ouled Aissa, 1800, Jijel, Algeria;1. Department of Mechanical Engineering, Maulana Azad National Institute of Technology Bhopal 462003, India;2. Department of Mechanical Engineering, Maulana Azad National Institute of Technology Bhopal 462003, India;3. Department of Mechanical Engineering, SV polytechnic college Bhopal, India;1. Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai 600077, India;2. Department of Automobile Engineering, Madras Institute of Technology, Chennai 600044, India;3. Department of Mechanical Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, A.P, 521230, India;1. Faculty of Materials Science and Engineering, Warsaw University of Technology, 141 Woloska St, 02-507, Warsaw, Poland;2. Faculty of Chemistry, Warsaw University of Technology, 3 Noakowskiego St, 00-664, Warsaw, Poland;3. Instytut Techniki Budowlanej, Ksawerów 21, 02-656, Warsaw, Poland;4. Faculty of Mechanical Engineering, Military University of Technology, 2 gen. S. Kaliskiego St., 00-908, Warsaw, Poland |
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| Abstract: | In this paper, we study the formation of general Group Technology cells based on the operation requirements of parts and operation capabilities of machines. Parts are first grouped into families by using a similarity coefficient based on common operation types. An integer model is then developed to solve the problem of machine group selection. The model takes into account machine cost, variable production cost, setup cost, and intracell material handling cost. A greedy heuristic, a minimum increment heuristic and a simulated annealing heuristic are proposed for solving the model more efficiently. The computational results have shown that the heuristic methods perform well when compared to the optimal solutions. The effect of changing cost structure on the performance of heuristic procedures is also investigated. |
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