Paper Title:
A Novel Variable Neighborhood Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problems
  Abstract

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. It is quite difficult to achieve optimal or near-optimal solutions with single traditional optimization approach because the multi objective FJSP has the high computational complexity. An novel hybrid algorithm combined variable neighborhood search algorithm with genetic algorithm is proposed to solve the multi objective FJSP in this paper. An external memory is adopted to save and update the non-dominated solutions during the optimization process. To evaluate the performance of the proposed hybrid algorithm, benchmark problems are solved. Computational results show that the proposed algorithm is efficient and effective approach for the multi objective FJSP.

  Info
Periodical
Advanced Materials Research (Volumes 118-120)
Edited by
L.Y. Xie, M.N. James, Y.X. Zhao and W.X. Qian
Pages
369-373
DOI
10.4028/www.scientific.net/AMR.118-120.369
Citation
G. H. Zhang, L. Gao, Y. Shi, "A Novel Variable Neighborhood Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problems", Advanced Materials Research, Vols. 118-120, pp. 369-373, 2010
Online since
June 2010
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Price
$32.00
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