A Hybrid PSO Algorithm for Job-Shop Scheduling Problems with Fuzzy Processing Time and Fuzzy Due Date

This paper presents a hybrid PSO (HPSO) algorithm to the solution of job-shop fuzzy scheduling problem. The proposed algorithm uses processing encoding random key to generate initial population, takes parameter uniformity crossover operator as particle swarm’s update operator, and evaluates each particle properties according to customer satisfaction, and then completes particle individual extremum and neighborhood extremum update according to the above-mentioned evaluation. The algorithm utilizes neighborhood knowledge to direct its local search procedure, which overcome the blindness or randomness introduced by meta-heuristics. Simulation results show that HPSO algorithm can speed up convergence as well as improve the quality of shop scheduling solution.