Solution Space Analysis and Feasible Genetic Algorithm for Assembly Job-Shop Scheduling Problems

Abstract:

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The classical job-shop scheduling problems (JSP) become assembly job-shop scheduling problems (AJSP) if assembly constraints are attached to them. The entire solution space size and the feasible one of AJSP are analyzed and obtained by utilizing combinational mathematics. It is proved that the feasible solution space takes extremely small portion of the entire one. To minimize the makespan of AJSP, genetic algorithm searching in feasible solution space (FGA) is proposed and designed, and the search range of FGA is limited to feasible solution space. Finally, benchmarks tests and results are given which demonstrate the advantage and efficiency of FGA.

Info:

Periodical:

Materials Science Forum (Volumes 626-627)

Edited by:

Dongming Guo, Jun Wang, Zhenyuan Jia, Renke Kang, Hang Gao, and Xuyue Wang

Pages:

705-710

DOI:

10.4028/www.scientific.net/MSF.626-627.705

Citation:

G.K. Zhao et al., "Solution Space Analysis and Feasible Genetic Algorithm for Assembly Job-Shop Scheduling Problems", Materials Science Forum, Vols. 626-627, pp. 705-710, 2009

Online since:

August 2009

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Price:

$35.00

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