Solving Job Shop Scheduling Problem by a Hybrid Genetic Algorithm

Abstract

In order to solve the job shop scheduling problem (JSSP) better, a new hybrid genetic algorithm (HGA), which combines genetic algorithm (GA) and simulated annealing algorithm (SA) is proposed. In HGA for JSSP, the fitness and target value of algorithm are represented by completion time of jobs. HGA proposed in this paper can avoid such disadvantages as premature convergence and low stability. Experimental results demonstrate that the proposed algorithm does not get stuck at a local optimum easily, and it is fast in convergence, simple to be implemented. At last, several examples testify the effectiveness of HGA for JSSP.