Research 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 customerdemand, many dynamic events have been occured in the real manufacturing. The dynamicscheduling problems have become one of the hot topics in the field of manufacturingsystem. Moreover, with the increasing rapid development of global economy,environmental problems are becoming the focus of attention. As a new sustainablemanufacturing mode, low carbon manufacturing is an effective way to realize the pledgefor conserving energy and reducing emissions in our country by2012. This paper focuseson the research of the dynamic job shop scheduling problem, the dynamic flexible jobshop scheduling problem and the multi-objective dynamic flexible job shop schedulingproblem based on low carbon.Dynamic events always occur in the real shop floor. It is important to keep thescheduling stability, improve the scheduling efficiency and make reasonable schedulingdecisions. According with the integer programming for the classical job shop schedulingproblem and the characteristic of the dynamic events, a mathematical model for thedynamic job shop scheduling problem is proposed. In order to improve the robust of thepredictive/active scheduling strategy, a predictive/active scheduling strategy with insertingidle time is proposed. The experimental results show that the proposed strategy can keepthe robust of the original schedule and improve the scheduling efficiency and thescheduling stability in small scale shop floor. However, the complete rescheduling strategyhas a better scheduling performance in large scale shop floor.An effective rescheduling approach has important impact on making decisions in realmanufacturing system. One hybrid algorithm which is mixed by the genetic algorithmwith strong global searching ability and tabu search with strong local searching ability hasbeen proposed to solving the dynamic job shop scheduling problem. The experimentalresults show that the new initialization can keep the population diversity and improve theglobal search ability. They also show that the hybrid genetic algorithm and tabu searchalgorithm has the good robustness.How to balance the scheduling efficiency and the scheduling stability is a keyproblem to solving the dynamic job shop problem in real shop floor. In this paper, amulti-objective mathematical model for dynamic job shop scheduling problem, whichcontains the scheduling efficiency and the scheduling stability, has been proposed. Ahybrid genetic algorithm and tabu search algorithm is proposed to solve themulti-objective dynamic job shop scheduling problem. The simulator generates the dynamic events for next phase at each rescheduling point. The hybrid algorithm optimizesthe problem and generates the prediction schedules. The experimental results show theeffectiveness and advantage of the proposed model and the proposed approach.Dynamic flexible job shop scheduling problem is one of the extension of dynamic jobshop scheduling problem. This paper inserts a mean quantity of operations to improve theevaluation system of dynamic flexible job shop scheduling problem. The mathematicalmodel for dynamic flexible job shop scheduling problem has been proposed. An effectivealgorithm, which mixes the genetic algorithm and variable neighborhood search algorithm,has been proposed to solve the multi-objective dynamic flexible job shop schedulingproblem. The experimental results show the feasible of the proposed approach. Theexperimental results also reveal that the curve of the shop load level and the schedulingperformance is U-shape. The ANOVA method shows that the shop load level, new jobarrivals 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 ofinternational politics, economy and the academic research. This paper designs the energyconsumption according to the operation-based processing unload energy consumption. Agoal programming model based on low carbon has been proposed. An improved geneticalgorithm with elitist strategy has been proposed to solve dynamic flexible job shopscheduling problem. The experimental results show that the minimization energyconsumption model can reduce the energy consumption and enhance the schedulingefficiency partly. Moreover, a multi-objective dynamic flexible job shop scheduling model,which contains the energy consumption, the scheduling efficiency and the schedulingstability, has been proposed. The experimental results show that the proposed model canreduce the energy consumption, improve the scheduling efficiency and keep thescheduling stability. Finally, the ANOVA method shows that the shop load level, new jobarrivals 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 enginecooling fan shop floor, the issues in the shop floor have been analyzed. The above researchresults have been applied into the shop floor to test and analyze.Finally, the research results achieved in the dissertation is summarized and futurework is generalized and looked forward.