A fuzzy-knowledge resource-allocation model of the semiconductor final test industry

Authors:
Kung-Jeng Wang;Y. -S. Lin;Chen-Fu Chien;J. C. Chen
Affiliations:
Department of Industrial Management, National Taiwan University of Science and Technology, 43, Sec.4, Keelung Road, Taipei 106, Taiwan, ROC;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan, ROC;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan, ROC;Department of Industrial Engineering, Chung Yuan Christian University, Chung-Li 320, Taiwan, ROC
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2009

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Abstract

The operations of the semiconductor final test industry are complicated and characterized by multiple-resource constraints that require simultaneous considerations. One of the most challenging production-planning decisions in the industry concerns an efficient allocation of resources that results in high manufacturing performance. Firms in the industry are thus eager to discover resource-allocation knowledge from large manufacturing databases. This study develops a novel model via the extraction of fuzzy-business rules from databases for obtaining resource-allocation knowledge as well as allocating resources efficiently. The proposed model uses both a genetic algorithm to find the best priority sequence of customer orders for resource allocation and, in accordance with the priority sequence of orders, a fuzzy-inference model to allocate the resources and to determine the order-completion times. Experiments showed that the proposed model can significantly reduce task tardiness.