Concurrent process/inspection planning for a customized manufacturing system based on genetic algorithm |
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Authors: | Yau-Ren Shiau Meng-Hung Lin Wen-Chieh Chuang |
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Affiliation: | (1) Department of Industrial Engineering and System Management, Feng-Chia University, 100 Wenhwa Road, Seatwen, P.O. Box 25-097, Taichung, Taiwan, 407, Republic of China;(2) Office of Research and Development, Feng-Chia University, Taichung, Taiwan, Republic of China |
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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 (i.e., 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 job-shop 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 near-optimal 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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Keywords: | Inspection planning Manufacturing resource allocation Process planning |
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