An Intelligent Method for Solving Flexible Job Shop Scheduling Problem
Q. Meng, L. Zhang, Y. Fan, H. Luo, H. Zhao
An efficient intelligent algorithm is proposed in this paper to solve the flexible job shop scheduling problem (FJSSP). The algorithm takes genetic algorithm (GA) as the main frame, and combines tabu search (TS) with simulated annealing (SA) to promote local search. The operation-based representation, novel crossover and mutation operation are introduced to
increase individual diversity. When the current solution exists in the tabu list, the algorithm performs local search incorporating TS and the SA method to explore the neighbourhood of the individuals. The experiments are carried out on 10 different scales instances and the results with other algorithms are compared. The results conclude that the proposed algorithm has advantages on both the quality and the time consuming which is effective to the actual FJSSP.