Differential PSO for Production Reactive Scheduling of Manufacturing Systems

Variety of solutions can be presented for scheduling problem with considering a manufacturing system. For doing so, it is necessary to define a job shop system for realmanufacturing system. For solving the scheduling system, the different genetic algorithms can be used which minimize theperformance parameters. The particle swarm optimization (PSO) proposes a local search heuristic that is introduced to exploit more latitude of search space to anchor the globaloptima. The PSO algorithm has fewer parameters than the genetic algorithms to adjust. In this paper first, a reactive realmanufacturing is defines then the particle swarm optimization with new changes to solve the scheduling problem is used. Finally, the obtained result is compared with Adaptive genetic algorithm (AGA).