Scheduling for the Flexible Job-Shop Problem Based on a Hybrid Genetic Algorithm

作者：Wang JinFeng^*; Fan XiaoLiang

来源：Sensor Letters, 2011, 9(4): 1520-1525.

DOI：10.1166/sl.2011.1676

• Flexible Job-Shop Scheduling
• Hybrid Genetic Algorithm
• Chromosome Representation
• Simulated Annealing

Scheduling for the flexible job-shop problem (FJSP) is very important. However, it is quite difficult to achieve an optimal solution to the FJSP with traditional optimization approaches. This paper examines the development and application of a hybrid genetic algorithm (HGA) to the FJSP. A hybrid algorithm is proposed which incorporates a local improvement procedure based on simulated annealing (SA) into a basic genetic algorithm. The incorporation of the local improvement procedure enables the algorithm to perform genetic search over the subspace of local optima. The algorithm is tested on instances of 8 jobs and 8 machines. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.

• 出版日期2011-8
• 单位华北电力大学