Research On The Solution Methods Of Integrated Process Planning And Scheduling

Process planning and Scheduling are two of the most important sub-systems in manufacturing systems. In traditional approach, process planning and scheduling were carried out sequentially. The researchers did not pay much attention on the integration of them. This approach has become an obstacle to improve the productivity and responsiveness of manufacturing systems. However, in fact, the integrated process planning and scheduling (IPPS) can greatly enhance the productivity of the manufacturing system. Therefore, IPPS has attracted more and more researchers and engineers.IPPS is one of the most complicated NP- Complete combinational optimization problems. After more than 20 years of development, researchers had proposed several methods. However, the state-of-the-art algorithms also can not solve this problem effectively. The main purpose of this dissertation is to do the deep research on IPPS problem and to explore the effective solution methods of this problem under different situations. Firstly, based on the mathematical model of job shop scheduling problem, the mathematical model of IPPS and an integration optimization strategy have been proposed. This strategy can guide the following research works of the dissertation. Then, according to this strategy, we do the research on each part detailedly: 1) Making deep research on the flexible process planning (FPP) problem with genetic algorithm (GA). In this part, the multi-parts representation method has been proposed. This method is more conducive to the design and operations of the genetic operators. The new crossover operator has been proposed to avoid the unreasonable solutions and improve the solution efficiency. Based on the features of this representation, the relative mutation operator has been designed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted. The experimental results show that the proposed approach is very effective; 2) Based on the previous works, making deep research on the IPPS problem. In this part, the flow chart of GA based on the previous integration optimization strategy has been proposed. In this approach, the operation-based representation method and active scheduling decoding method have been used for the scheduling problem. The crossover and mutation operators have been designed. Several experiments have been used to verify the feasibility and performance of the proposed approach. The results show that the research on IPPS is necessary and the proposed method is very effective to solve IPPS problem.The researches show that one single algorithm can not solve the complex combinational optimization problems effectively. And, the hybrid algorithms can provide more powerful searching ability. One hybrid algorithm which is mixed by the GA with strong global searching ability and tabu search with strong local searching ability has been proposed to solve IPPS problem. This algorithm can balance its diversification and intensification very well during the searching process. Based on the features of IPPS problem, effective neighborhood has been selected and modified. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some other works. The experimental results show that the hybrid algorithm has achieved significant improvement.In the real production environment, there are many multi-objective problems. However, in the current stage, the most researches on IPPS focus on the single objective problem. Therefore, the research on the multi-objective IPPS problem is necessary and has been done in this dissertation. Based on the previous works on the single objective IPPS problem and multi-objective evolutionary algorithm, one solution method has been proposed to solve the multi-objective IPPS problem. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted. The experimental results show that the proposed method is effective.In traditional approach, the most researches focus on the static IPPS problem. However, in the real production environment, there exist many unexpected events to interfere in the normal production process. Therefore, the results of the static IPPS can not fit the real production very well. In this dissertation, the research on the dynamic IPPS has been done. It is the extension of the static IPPS. Based on the previous work of the static IPPS and proposed optimization algorithms, one dynamic scheduling strategy based on the improved GA has been proposed. To verify the feasibility and performance of the proposed approach, it has been applied to solve several dynamic events in the real production environment. The results show that the proposed method is effective.The IPPS simulation system has been designed and developed based on the above research works. This system can use the proposed algorithms to solve the FPP and IPPS problems. One practical case study has been used to verify the feasibility and performance of this system.Finally, the researches in the dissertation are summarized and some future research directions have been presented.