Research On Method Of Job Shop Dynamic Stochastic Rescheduling Based On Real-Time Status

Analyzing the uncertainties caused by complex workshop production environment and kinds of stochastic interruptions during the production process and classifing the unexpective instances in production, to obtain all kinds of messages related to the interruptions. Using appropriate methods related to the interruptions in real time, to collect real-time data and monitor the real-time status in production. Classify the interruptions into dominant interruptions and recessive interruptions, and adopt initiative and passive rescheduling driven rule respectively to respond to the interruptions directly. Throughout researching the rescheduling threshold using mathematical technique to get rescheduling time , to enhance the stability of production system, at the meantime to filtrate unnecessary rescheduling. 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 the vacancy between working procedures and make good use of equipments . A Hybrid Particle Swarm Optimization scheduling algorithm is given. Finally simulation results show the efficiency of the selecting rule using the Hybrid Particle Swarm Optimization scheduling algorithm.