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https://doi.org/10.1007/978-3-663-08850-9and an unwillingness to change in a market that has become increasingly customer-oriented. Fashion-led markets necessitate good design, short lead times, flexibility, and consistent high quality. The major strategic response to this challenge has been to participate in quick response (QR) programs t

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Pers?nlichkeit braucht Tugendeniate classification of parts into families and machines into cells, many researchers believe that utilizing scheduling procedures which capitalize on the unique features of cellular manufacturing is essential to counter its disadvantages and enhance the likelihood of successful implementation (e.g.,

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https://doi.org/10.1007/3-540-28984-4This chapter focuses on two, relatively new approaches for the part-machine grouping problem: artificial neural networks and fuzzy models. Neural networks and fuzzy models were introduced in group technology (GT) literature during 1990s to overcome some of the limitations of conventional approaches.

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https://doi.org/10.1007/978-3-658-33616-5facturing performance parameters such as cycle time, quality and costs can be improved by capitalizing on similarities that can be found in such things as workpiece design, assembly methods, purchasing of materials, or tooling designs (Apple 1977).