Modeling And Optimization Of Integrated Planning And Scheduling Under Uncertain

The integrated planning and scheduling problem is NP-hard. Planning and scheduling’s primarily point is in adequately utilizing materials, human resources and equipment in order to increase productivity and create more benefits for companies. This paper addresses the solutions of an integrated multi-product, multi-site, multi-market planning and scheduling model under uncertain. With the objective of maximizing the total profits in planning time horizon under uncertain prices and demand, the planning section determines the amount for each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period; the scheduling section determines the products sequence, start and end time of each product running in each manufacturing site during each scheduling time period. This includes:(1) Researching on the modeling and optimization of integrated planning and scheduling problems under certain or uncertain worked by other researchers. An integrated multi-product, multi-site, multi-market planning and scheduling model is studied in-depth.(2) The main difficulty integrated planning and scheduling model is NP-hard. which is hard to solve by traditional method. Genetic Algorithm (GA) and Cuckoo Search Algorithm (CS) are studied and an improved Cuckoo Algorithm is proposed. Basis improving strategies is to focus on the distances between individuals in population space, and if the distance is relatively small, then randomly switch the position of these individuals. By using Genetic Algorithm and Modified Cuckoo Searching Algorithm (MCS) to optimize multi-products planning and scheduling integrated mathematic model under certain, the simulating results show the effectiveness of GA and MCS.(3) Integrated multi-product, multi-site, multi-market planning and scheduling models under prices uncertain are proposed and solved by Genetic Algorithm. When comes to the uncertain demand, Integrated multi-product, multi-site, multi-market planning and scheduling models under demand uncertain are proposed and solved by Genetic Algorithm. The simulation results show the effectiveness of the models and algorithm.(4) Environment changes a lot in the production process. Project manager always expects the proposed manufacturing scheme is strongly robust. Six types of bounded sets are used to describe the uncertain product prices and demand. There are several preference robust models are proposed in this paper based on different sets which used to describe the uncertain product prices and demand. The proposed models are solved by Genetic Algorithm. The simulation results show the effectiveness of the models.