Reasearch On Theories And Methods For Dynamic Job Shop Scheduling Based On Predictive-reactive Scheduling

With the increasing competition of global market and the diversity of customer demand, many dynamic events have been occured in the real manufacturing. The dynamic scheduling problems have become one of the hot topics in the field of manufacturing system. Moreover, with the increasing rapid development of global economy, environmental problems are becoming the focus of attention. As a new sustainable manufacturing mode, low carbon manufacturing is an effective way to realize the pledge for conserving energy and reducing emissions in our country by2012. This paper focuses on the research of the dynamic job shop scheduling problem, the dynamic flexible job shop scheduling problem and the multi-objective dynamic flexible job shop scheduling problem based on low carbon.Dynamic events always occur in the real shop floor. It is important to keep the scheduling stability, improve the scheduling efficiency and make reasonable scheduling decisions. According with the integer programming for the classical job shop scheduling problem and the characteristic of the dynamic events, a mathematical model for the dynamic job shop scheduling problem is proposed. In order to improve the robust of the predictive/active scheduling strategy, a predictive/active scheduling strategy with inserting idle time is proposed. The experimental results show that the proposed strategy can keep the robust of the original schedule and improve the scheduling efficiency and the scheduling stability in small scale shop floor. However, the complete rescheduling strategy has a better scheduling performance in large scale shop floor.An effective rescheduling approach has important impact on making decisions in real manufacturing system. One hybrid algorithm which is mixed by the genetic algorithm with strong global searching ability and tabu search with strong local searching ability has been proposed to solving the dynamic job shop scheduling problem. The experimental results show that the new initialization can keep the population diversity and improve the global search ability. They also show that the hybrid genetic algorithm and tabu search algorithm has the good robustness.How to balance the scheduling efficiency and the scheduling stability is a key problem to solving the dynamic job shop problem in real shop floor. In this paper, a multi-objective mathematical model for dynamic job shop scheduling problem, which contains the scheduling efficiency and the scheduling stability, has been proposed. A hybrid genetic algorithm and tabu search algorithm is proposed to solve the multi-objective dynamic job shop scheduling problem. The simulator generates the dynamic events for next phase at each rescheduling point. The hybrid algorithm optimizes the problem and generates the prediction schedules. The experimental results show the effectiveness and advantage of the proposed model and the proposed approach.Dynamic flexible job shop scheduling problem is one of the extension of dynamic job shop scheduling problem. This paper inserts a mean quantity of operations to improve the evaluation system of dynamic flexible job shop scheduling problem. The mathematical model for dynamic flexible job shop scheduling problem has been proposed. An effective algorithm, which mixes the genetic algorithm and variable neighborhood search algorithm, has been proposed to solve the multi-objective dynamic flexible job shop scheduling problem. The experimental results show the feasible of the proposed approach. The experimental results also reveal that the curve of the shop load level and the scheduling performance is U-shape. The ANOVA method shows that the shop load level, new job arrivals has a statistical influence on the scheduling efficiency and the scheduling stability.With global warming and the increasing rapid development of global economy, reducing energy consumption has become one of the hot topics in the research of international politics, economy and the academic research. This paper designs the energy consumption according to the operation-based processing unload energy consumption. A goal programming model based on low carbon has been proposed. An improved genetic algorithm with elitist strategy has been proposed to solve dynamic flexible job shop scheduling problem. The experimental results show that the minimization energy consumption model can reduce the energy consumption and enhance the scheduling efficiency partly. Moreover, a multi-objective dynamic flexible job shop scheduling model, which contains the energy consumption, the scheduling efficiency and the scheduling stability, has been proposed. The experimental results show that the proposed model can reduce the energy consumption, improve the scheduling efficiency and keep the scheduling stability. Finally, the ANOVA method shows that the shop load level, new job arrivals has a statistical influence on the scheduling efficiency and the scheduling stability.Based on the research word mentioned above and the real conditions in the engine cooling fan shop floor, the issues in the shop floor have been analyzed. The above research results have been applied into the shop floor to test and analyze.Finally, the research results achieved in the dissertation is summarized and future work is generalized and looked forward.