Dynamic rescheduling optimization of job-shop under uncertain conditions

The uncertainties caused by complex and changeable workshop production environment and various stochastic disturbances during the production process are analyzed. Disturbances are classified into dominant disturbances and recessive disturbances, and initiative and passive rescheduling driven rules are adopted respectively to respond to various disturbances. A rescheduling optimization set is built, combined with rolling horizon optimization method, to simplify the large scale dynamic scheduling problem. A selecting rule of jobs is proposed to reduce vacancy between working procedures. A hybrid particle swarm optimization (PSO) scheduling algorithm is given. Finally simulation results show the efficiency of the method of optimization of dynamic rescheduling with the hybrid PSO.