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.

• Periodical: Advanced Materials Research (Volumes 118-120)
• Pages: 369-373
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.118-120.369
• Online since: June 2010
• Authors: Guo Hui Zhang, Liang Gao, Yang Shi
• Keywords: Flexible Job-Shop Scheduling, Genetic Algorithm (GA), Multi-Objective, Variable Neighborhood Search

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