Research On Intelligent Production Scheduling Optimization Methods And Its Applications

As the most weak and difficult chain in the production management, production scheduling has become the bottle-neck problem in the research on the computer integrated manufacturing system. Since 1950's, many significant results have been presented in production scheduling problems widely studied on. However, there are still no systemic theories and methods used to solve the problems due to the difficulties. For many years, the researchers and personnel in the enterprises have fixed attention on the issue how to combine the classical scheduling theories with the actual production to improve the accuracy, validity, and real time performance of the production control for the enterprises. Because a multi-agent system is made up of the autonomous agents that collaborate and cooperate dynamically to solve the problem. The autonomous, distributed and dynamic features of the multi-agent system can fit the requirement of the complex, flexible, robust and dynamic manufacturing scheduling. Consequently, the multi-agent system is a good method to solve the above problem. The multi-agent production scheduling system model and everal job-shop scheduling methods were mainly investigated in the dissertation, such as the improved static methods based on Hopfield neural networks and the immune algorithm, and the multi-agent dynamic method. The main contributions of the paper are as follows:(1) The research background and significance of the thesis was introduced, and the description and categorization of production scheduling problem were given. Based on summing up a large number of domestic and foreign literatures, related research work on the problems was focused on summary, and the existing problems in the production scheduling research was analyzed.(2) Aiming at the complex production scheduling problems in the uncertain and dynamic manufacturing environment, and especially the short-term, agile and dynamic scheduling problems, the production planning and shop scheduling system model based on the multi agents was built, where the functions of management agent, resource agent, task agent and computation agent were given. The negotiation of the improved contract-net protocol among the agents was proposed in production planning optimization assignment for parallel shops of the enterprise. The realization of the multi-agent planning optimization model was given, and the simulation experiment approved the validity of the model.(3) Because the job-shop scheduling method based on Hopfield neural network is apt to search infeasible solutions, discrete Hopfield neural network (DHNN) method with the operation representation was presented, and the new energy function consisting of row inhibition, column inhibition, global inhibition and objective inhibition was given. So the neural network would rapidly convergence to a feasible solution satisfying with resource and sequence restrictions. In order to search the global solution, the discrete Hopfield neural network with transient chaos method for the job-shop scheduling was developed, where the simulated annealing mechanism was introduced into DHNN. The simulation results of benchmark problems show that its optimization performance was better than the other methods mentioned. In addition, the improved neural network optimization methods were applied to solve an actual scheduling problem from a machinery factory.(4) Based on the adaptive mechanism of bacterin extraction and vaccination, the adaptive immune algorithm for the job shop scheduling was presented. And then its optimization performance, bacterin extraction and vaccination methods, and coding modes were analyzed in the simulation experiments. In order to improve the performance of the algorithm, the immune evolution algorithm based on the multi-agent system was developed. It mainly consisted of several operators: competition operator among the agent and its neighbors, self-study operator of the optimal agent, adaptive bacterin extraction and vaccination operator, cross and mutation operator, and simulated annealing operator. The position of the agent was updated with those operators in the solution space, and thus it would accurately search the global optimal. Then, based on the actual production features of paper basin shops, the model of the fuzzy and flexible job-shop scheduling problem with various batches was given. Finally, the multi-agent immune algorithm was applied to solve a scheduling example from a certain paper basin shop, and the results showed its validity.(5) The coordination mechanism based on ant intelligence and reinforcement learning was developed for the dynamic manufacturing environment, where the adaptive agent was built to realize the dynamic scheduling of jobs. The example simulation showed that the method was effective in the changeable environment of orders and machines. Based on the techniques and restrictions of printing and dyeing production process, the dyeing shops scheduling problem model was built, and then the multi-agent dynamic scheduling method of dyeing shops scheduling was also presented. The scheduling example proved that the adaptability of the method for the changeable production environment.(6) Based on the above-mentioned academic research, an intelligent production planning and shop scheduling system based on the multi agents was developed to satisfy with the urgent requirement of the enterprise production management. The system was based on the classical production and management features of discrete manufacturing enterprises whose production mode was order-production, multi-varieties and small batch. The system supported the plan optimization assignment of parallel shops and the uncertain job shop scheduling with fuzzy processing time and fuzzy due date, and provided the intelligent scheduling methods, such as genetic algorithms, neural networks, immune algorithms, and so on. As a subsystem of the integrated application system for the enterprise, it was successfully applied to a certain electroacoustic enterprise in Zhejiang province.Finally, the whole research work of the dissertation was summarized, and the future of production scheduling problem research and its application was given.