Paper Info
Title
A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem
Abstract
To improve capital effectiveness in light of demand fluctuation, it is increasingly important for high-tech companies to develop effective solutions for managing multiple resources involved in the production. To model and solve the simultaneous multiple resources scheduling problem in general, this study aims to develop a genetic algorithm (bvGA) incorporating with a novel bi-vector encoding method representing the chromosomes of operation sequence and seizing rules for resource assignment in tandem. The proposed model captured the crucial characteristics that the machines were dynamic configuration among multiple resources with limited availability and sequence-dependent setup times of machine configurations between operations would eventually affect performance of a scheduling plan. With the flexibility and computational intelligence that GA empowers, schedule planners can make advanced decisions on integrated machine configuration and job scheduling. According to a number of experiments with simulated data on the basis of a real semiconductor final testing facility, the proposed bvGA has shown practical viability in terms of solution quality as well as computation time.
Year
DOI
Venue
2012
10.1007/s10845-011-0570-0
Journal of Intelligent Manufacturing
Keywords
Field
DocType
Total resource management,Manufacturing management,Flexible manufacturing systems,Scheduling,Genetic algorithm
Mathematical optimization,Job shop scheduling,Fair-share scheduling,Scheduling (computing),Scheduling (production processes),Genetic algorithm scheduling,Job scheduler,Rate-monotonic scheduling,Engineering,Dynamic priority scheduling
Journal
Volume
Issue
ISSN
23
6
0956-5515
Citations 
PageRank 
References 
21
0.99
20
Authors
4
Name
Order
Citations
PageRank
Jei-Zheng Wu1959.40
Xin-Chang Hao2665.19
Chen-Fu Chien362358.23
Mitsuo Gen41873130.43