Shop Scheduling In Uncertain Environment

As the development of science and information technology, market competition between enterprises is increasingly fierce. Multi–item and small–batch manufacturing model has become the trend of the development of enterprises due to its rapid response to market demand. It has special advantages, but also has to face several problems. Such as repetitive low production, long manufacturing lead times, production line changes frequently, management confusion and so on. The problems summarized above bring more uncertainty factors. The uncertainty of the production process has become bottleneck for scheduling theory applied to actual production. Therefore, how to build a more realistic shop scheduling model and design the appropriate optimization algorithm is a meaningful work.In this thesis, H company with manufacturing model of multi-item and small–batch has been studied. Based on the characteristics and problems of H company’s production planning and scheduling, we summarized the factors causing delays in its delivery. In the actual production, the shop scheduling problem is usually multi-objective problem. And we studied parallel machine scheduling problem with multi-objective in uncertain environment.To begin with, the processing times are assumed to be uncertain variables with known uncertainty distributions, a parallel machine scheduling model is proposed within the framework of uncertainty theory. In this model, makespan and expected completion time of key workpiece are integrated into one objective function. Next, a intelligent algorithm is designed for solving the uncertain programming model we have mentioned above. Finally, some numerical example is given to illustrate the feasibility of the proposed model and algorithm.