PSO based scheduling algorithm for open-shop scheduling problem

Open-shop scheduling problem (OSP) is an important scheduling problem and has wide engineering applications in manufacturing. Optimization algorithms are important research content in scheduling theory. Artificial intelligence based meta-heuristic algorithms are effective methods for this problem. A new meta-heuristic based on particle swarm optimization (PSO) is proposed to obtain optimized open shop schedule. First, the limitation of information sharing mechanism in PSO model is discussed, and then new information sharing mechanism based on swarm intelligence is put forward. Based on the new information sharing mechanism, a new PSO based on scheduling algorithm-PSO-OSP is proposed. The proposed algorithm utilizes neighborhood knowledge to direct its local search procedure which can overcome the blindness or randomness introduced by meta-heuristics. Finally, OSP benchmarks are used to test its efficiency. Simulation results show that the new proposed algorithm can improve the convergence speed and obtain optimized open shop schedules.