A Hybrid Genetic Algorithm for Flexible Job-Shop Scheduling Problem

2014 ◽  
Vol 889-890 ◽  
pp. 1179-1184 ◽  
Author(s):  
Shuang Xi Wang ◽  
Chao Yong Zhang ◽  
Liang Liang Jin
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this paper, a hybrid genetic algorithm is presented for the flexible job-shop scheduling problem with makespan criterion. A new machine assignment strategy is proposed to improve the initial population. A modified coding scheme is presented, and a population improvement strategy is performed when the best solution of the population did not improve during some generations. This hybrid algorithm is tested on a series of benchmarks instances. Experimental results show that this hybrid algorithm is efficient and competitive compared to some existing methods.

A Novel Variable Neighborhood Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problems

2010 ◽  
Vol 118-120 ◽  
pp. 369-373 ◽  
Author(s):  
Guo Hui Zhang ◽  
Liang Gao ◽  
Yang Shi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective

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.

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