Study on the combination of genetic algorithms and ant Colony algorithms for solving fuzzy job shop scheduling problems

By using a single algorithm to deal with fuzzy job shop scheduling problems, it is difficult to get a satisfied solution. In this paper we propose a combined strategy of algorithms to solve fuzzy job shop scheduling problems. This strategy adopts genetic algorithms and ant colony algorithms as a parallel asynchronous search algorithm. In addition, according to the characteristics of fuzzy job shop scheduling, we propose a concept of the critical operation, and design a new neighborhood search method based on the concept. Furthermore, an improved TS algorithm is designed, which can improve the local search ability of genetic algorithms and ant colony algorithms. The experimental results on 13 hard problems of benchmarks show that, the average agreement index increases 6.37% than parallel genetic algorithms, and increases 9.45% than TSAB algorithm. Tabu search algorithm improves the local search ability of the genetic algorithm, and the combined strategy is effective.