Research On The Problem Of Multi-objective Flexible Job Shop Scheduling Optimization

The classical job shop scheduling Problem (JSP) has always being studied by researchers and therefore abundant theories are yielded. However, they are hard to put into practice because of the unrealistic assumptions in setting up the model of JSP. With technologies advance, flexible and multi-objective is the direction of job shop scheduling problem research field which has a long history. Multi-objective flexible job shop scheduling problem (FJSP) is an important extension of JSP, which takes into account not only the flexibility of machine availability but also the different expectations from different departments. Therefore, research on the multi-objective FJSP is significant both in theory and in practice.On the basis of the technical review on the domestic and foreign research, combining the actual job shop operation, the multi-objective FJSP is studied thoroughly and systematically. The problem of multi-objective flexible job shop scheduling optimization of production is studied, where multi-objects of make span, total tardiness, cost and equipment utilization rate (total and maximum machine tool loads) are concerned, based on the research on the workshop scheduling theories and approaches. The main work of this paper is as follows:(1) Directing against the limitation of classical job-shop scheduling and then combining the actual conditions of the workshop, the multi-objective flexible job shop scheduling problem optimization model was built, where time, cost, delivery satisfaction and equipment utilization rate were all concerned.(2) Because the traditional genetic algorithm and particle swarm optimization have localizations in the solution to multi-objective flexible job shop scheduling problem, aiming at improving searching efficiency and searching quality, multi-objective hybrid algorithm combining both advantages of particle swarm optimization and genetic algorithm is presented. This paper designs application of the algorithm in optimizing multi-objective flexible job-shop scheduling.(3) A simulation experiment is carried out to illustrate that the proposed hybrid genetic algorithm could solve the general multi-objective flexible job shop scheduling problem problem effectively. Finally, from the fact of production, two examples of the expanded multi-objective flexible job shop scheduling optimization in production are addressed. The experimental results can play a definite part in directing production.