Research On Production Scheduling Problem And Intelligent Optimization Algorithm

With the globalization of economy and the development of science and technology, the external environment of manufacturing enterprises becomes more and more complex and volatile, such as rapid market changes, increased competitions and more personalized and diverse customer needs. Production scheduling problem is a core issue of manufacturing system. A good scheduling can shorten the production cycle; improve enterprises'production efficiency and raise enterprises'competitiveness. Therefore, the research on the scheduling problem is important.There are many types of production scheduling, most of which are NP problems. Researchers have been working hard over the years to try to work out an optimal method to solve these problems. In recent years, with the development of some swarm optimization algorithms (such as genetic algorithm, evolutionary programming, differential algorithm, particle swarm optimization and ant colony algorithm, etc) and some neighborhood search algorithms (such as simulated annealing, taboo search etc.), new ideas and means were available for people to solve the production scheduling problems. At the same time many scholars begin to research these algorithms to solve scheduling problem. Based on the above mentioned, this dissertation aims to discuss the following aspects.A hybrid particle swarm optimization algorithm(PSO) is proposed to minimize the completion time of the job shop problem. In this algorithm, the particle is encoded with random key and an improved decoding algorithm is proposed to produce active scheduling. Since particle swarm optimization has a strong global search ability and a poor local search ability, in order to improve the local search ability of PSO, three simulated annealing algorithms which are based on different neighborhood structures are proposed and hybrid with PSO. At last, the computational results show the effectiveness of the algorithm.A hybrid particle swarm optimization algorithm (HPSO) is proposed to solve the flexible job shop scheduling problem(FJSP). In the algorithm, different encoding methods were proposed for assignment and sequence. In order to ensure the legitimacy of code for assignment, the updating formula of particles is changed. In order to improve the efficiency of algorithm, the initialization algorithm based on device and sequence is proposed to improve the quality of the initial population of HPSO. In order to improve the local search ability of algorithm, four SA algorithms based on different neighborhood search strategy are proposed and mixed with PSO. At last, the computational results show the effectiveness of the algorithm. Taking into account the strong coupling between assignment and sequence, co-evolution particle swarm optimization algorithm is proposed to solve FJSP. In the algorithm, assignment and sequence are treated as two variables. PSO algorithm will optimize them respectively and estimate them according each other. The computation results show that CPSO is better but slower than HPSO in the optimal.A new method using the distance of adjacent jobs for calculating makespan is proposed to large-scale no-wait flow shop scheduling. At the same time, an iterative neighborhood search algorithm is put forward. Since it reduces the time complexity, the efficiency is greatly improved. In order to avoid falling into local optimum, variable neighborhood search algorithm is used. As a result, the probability of finding the global optimal solution is enhanced. The computation results show that the algorithm for large-scale no-wait flow-shop scheduling is very practical and the solution is better.Based on the research of some hybrid computations, optimization mechanism, model of static hybrid algorithm, description and storage mode of algorithm knowledge for iterative search algorithm, the framework of iterative search algorithm and hybrid algorithm based on iterative algorithm is proposed. At last, the multi-agent technology is used to achieve it and the simulation experiment suggests the effectiveness and adaptability of the system.