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Title of Journal: Int J Adv Manuf Technol

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Abbravation: The International Journal of Advanced Manufacturing Technology

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Springer-Verlag

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DOI

10.1016/0304-5102(85)85133-6

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1433-3015

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Concurrent process/inspection planning for a custo

Authors: YauRen Shiau MengHung Lin WenChieh Chuang
Publish Date: 2006/05/23
Volume: 33, Issue: 7-8, Pages: 746-755
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Abstract

Producing products with multiple quality characteristics is always one of the concerns for an advanced manufacturing system To assure product quality finite manufacturing resources ie process workstations and inspection stations could be available and employed The manufacturing resource allocation problem then occurs therefore process planning and inspection planning should be performed Both of these are traditionally regarded as individual tasks and conducted separately Actually these two tasks are related Greater performance of an advanced manufacturing system can be achieved if process planning and inspection planning can be performed concurrently to manage the limited manufacturing resources Since the product variety in batch production or jobshop production will be increased for satisfying the changing requirements of various customers the specified tolerance of each quality characteristic will vary from time to time Except for finite manufacturing resource constraints the manufacturing capability inspection capability and tolerance specified by customer requirement are also considered for a customized manufacturing system in this research Then the unit cost model is constructed to represent the overall performance of an advanced manufacturing system by considering both internal and external costs Process planning and inspection planning can then be concurrently solved by practically reflecting the customer requirements Since determining the optimal manufacturing resource allocation plan seems to be impractical as the problem size becomes quite large in this research genetic algorithm is successfully applied with the realistic unit cost embedded The performance of genetic algorithm is measured in comparison with the enumeration method that generates the optimal solution The result shows that a nearoptimal manufacturing resource allocation plan can be determined efficiently for meeting the changing requirement of customers as the problem size becomes quite large

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