An effective immune particle swarm optimization algorithm for scheduling job shops

To solve the job shop scheduling problem with the objective of minimizing total weighted tardiness, an immune particle swarm optimization algorithm based on bottleneck job identification is presented. First, bottleneck characteristic values are defined to describe the criticality of each job on the final scheduling performance. Then, a fuzzy inference system is employed to evaluate the characteristic values based on human experience abstracted from practical scheduling environment. Finally, an immune mechanism is designed according to the bottleneck information and the idea that bottleneck jobs which can cause considerable deterioration to the overall performance measures should be considered with higher priority. Numerical computations are conducted with a particle swarm optimization algorithm which utilizes the immune mechanism. Computational results for problems of different scales show that the proposed algorithm achieves effective results by accelerating the convergence of the optimization process and the proposed bottleneck identification procedure reasonably reflects the features of both the objective function and the current optimization stage.