Paper Title:
Solution Space Analysis and Feasible Genetic Algorithm for Assembly Job-Shop Scheduling Problems
  Abstract

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, F.J. Wang, W. Liu, X.H. Lu, "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
$32.00
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