Applied research of improved hybrid discrete PSO for dynamic job-shop scheduling problem

By providing a detailed analysis of the particle swarm optimization (PSO) principle and job-shop scheduling problems, this paper presents a new hybrid discrete GAPSO combining the genetic strategy. Adjusting factors are introduced to regulate the generation of convergence; the proposed algorithm is tested by a set of benchmark problems. The results obtained show good convergence of the algorithm. On this basis, a new event-driven strategy for dynamic JSP is proposed, with regard to some uncertain dynamic events like inserting new jobs and machine failures, the proposed algorithm can reschedule once there occur uncertain dynamic events. The results of simulation have confirmed the effectiveness and feasibility of the improved hybrid discrete GAPSO algorithm.