The RFID Network Configuration And Optimization Scheduling Algorithm Research Of The Workshop Production

Production scheduling is the core of the workshop management which plays a key role on optimizing the utilization of resources, improving the work efficiency and saving costs. But a good scheduling system should not merely deal with the static datas but also can come up with a scheduling scheme when datas change or machines fail. Radio frequency identification technology identifies targets and gets the relevant datas through radio frequency signals. So in this paper, the RFID technology is adopted to establish a RFID data collection network to collect management and operation datas. Firstly, static job shop scheduling problem is studied, and then the dynamic scheduling problem is studied considering of the data collection network’s real-time datas. The thesis’ s works mainly includes the following four aspects:(1)To solve the problem of the low production data accuracy and long gathering feedback cycle, analyzing workshop data acquisition objects and methods, introducing the RFID system and its basic principles, and establishing data collection network to realize the unique identification and tracking of workshop process datas, so as to provide a complete data source for the dynamic scheduling.(2)According to the characteristics of the application of RFID network in the workshop, the working manner and coverage area of RFID readers’ antenna are analyzed. A novel optimization algorithm called velocity differential mutation-particle swarm algorithm is set up. The velocity mutation operation is added to the PSO algorithm to get rid of the bondage of local pole. The simulation results show the high efficiency of the algorithm in enhancing population diversity, optimizing fitness, and improving the efficiency of the network economy.(3)In view of the job shop scheduling problem, a job shop scheduling model is built and a novel optimization algorithm, named as cooperative hybrid particle swarm optimization is presented. Gravitational search algorithm is embedded to jump out of local optimum timely and guarantee the global optimum when the PSO evolution process falls into premature convergence.The proposed algorithm is performed for JSP typical test cases. The simulation results show the CHPSO algorithm obtains higher efficiency than PSO and GA algorithm for solving JSP.(4)In view of the flexible job-shop scheduling problem, a discrete random particle swarm optimization algorithm is presented which redefines the particles’ updating way. The simulation results show the discrete random particle swarm optimization algorithm can obtain high efficiency for solving FJSP. Finally, according to the datas from the data collection network, combined with dynamic scheduling strategy, simulation experiments verify that the algorithm can complete the job shop scheduling on the condition that machines malfunction and emergency workpiece disturbance.