Research On Job Shop Scheduling Problem Based On Energy Saving Mechanism

Under the background of protecting environment and saving energy of the globalization, it is urgent that how to realize energy saving and emission reduction in the Chinese manufacturing industry.Energy-efficient scheduling is an effective management strategy. Selecting different processing machines and changing processing sequence of the opearations while maintaing the level of equipment performance are to realize the energy saving. Hence, in this paper, the scheduling optimization of energy consumption as the main line is considered. A process planning scheduling with single objective optimization and the machines with flexible multi-objective optimization problem of job shop are studied, respectively. At the same time, the flexible job shop scheduling problem with energy consumption is investigaged in the dynamic environment.The major research work is described as follows:1. The research theory on job shop scheduling is summarized, and the classification, characteristics and optimization algorithm of job shop scheduling are also introduced in this paper. In addition, based on the analysis of the energy consumption in workshop scheduling, the energy consumption model of job shop scheduling is established.2. The process planning with energy consumption model is proposed and an improved is designed to improve the performance of the workshop scheduling problem. First, two objective optimization functions, i.e., the completion time and energy comsuption are established. Then, an improved hybrid simulated annealing and genetic algorithm is presented to investigate the relationship between the completion time and energy consumption. Finally, the feasibility of the algorithm is verified by comparison with a set of numerical examples and other algorithms.3. The multi-objective optimization problem of flexible job shop with energy consumption is studied. First, based on the description of the energy consumption and the makespan in the flexible job shop scheduling problem, the mathematical model of the optimization objectives is addressed. Second, an improved hybrid algorithm is proposed to solve the problem. In particular, PSO algorithm is introduced to the information sharing mechanism, which improves the crossover operator of genetic algorithm; the traditional simulated annealing temperature update function is replaced by a Hill function, which is used as the mutation opetator of genetic algorithm to avoid falling into premature convergence. Hence, a set of Pareto solutions can be obtained to provide a scheduling scheme for decision makers. Finally, the algorithm is verified by fully flexible and partially flexible scheduling examples, and the effectiveness of the improved algorithm is proved. In addition, the algorithm is applied to the actual case and good results are achieved.4. The dynamic scheduling model with the energy consumption is discussed by adding unexpected events to the shop scheduling. First, with delivery time as constraint conditions, the energy cost and completion time as the optimization objective is proposed by simulating the actual production scheduling environment. Using the rolling window technology, an improved algorithm is considered to study the shop scheduling problem with unexpected events, such as job insert and machine failure. Finally, the effectiveness and feasibility of the approach are verified by an example.