In this study three different methodologies are developed, (i) a similarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a state-of-the-art part clustering algorithm is proposed to improve the solution quality. (ii) A dissimilarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a novel approach based on a population based heuristic technique is proposed to improve the solution quality. (iii) A similarity coefficient based technique is exploited to form machine cells and part families. Thereafter a Simulated Annealing (SA) based metaheuristic algorithm is utilized to enhance the quality of the solutions obtained.
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Tamal Ghosh is a PhD Research Scholar in the Department of Production Engineering, Jadavpur University, India. He has also obtained his Master of Technology (M.Tech) in Industrial Engineering from West Bengal University of Technology and Bachelor of Technology (B.Tech) in Computer Engineering from National Institute of Technology Calicut India.
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ghosh TamalTamal Ghosh is a PhD Research Scholar in the Department of Production Engineering, Jadavpur University, India. He has also obtained his Master of Technology (M.Tech) in Industrial Engineering from West Bengal University of. Codice articolo 5142700
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this study three different methodologies are developed, (i) a similarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a state-of-the-art part clustering algorithm is proposed to improve the solution quality. (ii) A dissimilarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a novel approach based on a population based heuristic technique is proposed to improve the solution quality. (iii) A similarity coefficient based technique is exploited to form machine cells and part families. Thereafter a Simulated Annealing (SA) based metaheuristic algorithm is utilized to enhance the quality of the solutions obtained. 120 pp. Englisch. Codice articolo 9783659245763
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this study three different methodologies are developed, (i) a similarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a state-of-the-art part clustering algorithm is proposed to improve the solution quality. (ii) A dissimilarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a novel approach based on a population based heuristic technique is proposed to improve the solution quality. (iii) A similarity coefficient based technique is exploited to form machine cells and part families. Thereafter a Simulated Annealing (SA) based metaheuristic algorithm is utilized to enhance the quality of the solutions obtained. Codice articolo 9783659245763
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Taschenbuch. Condizione: Neu. Neuware -In this study three different methodologies are developed, (i) a similarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a state-of-the-art part clustering algorithm is proposed to improve the solution quality. (ii) A dissimilarity coefficient based approach is adopted to obtain the initial machine part grouping solution. Thereafter a novel approach based on a population based heuristic technique is proposed to improve the solution quality. (iii) A similarity coefficient based technique is exploited to form machine cells and part families. Thereafter a Simulated Annealing (SA) based metaheuristic algorithm is utilized to enhance the quality of the solutions obtained.Books on Demand GmbH, Überseering 33, 22297 Hamburg 120 pp. Englisch. Codice articolo 9783659245763
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