Optimizing Multi-Site Production Planning and Scheduling with a Hybrid Genetic Algorithm

As the economic environment continues to change rapidly, so does the demand placed on industries to meet ever expanding orders. In order to meet this demand, the need to expand is matched by the need to utilize existing facilities, and increase efficiency. As a result, conventional order management programs often no longer suffice. Single-site planning has developed into multi-site planning, leading to distance problems between sites as well as problems regarding information transfers between them. This study aims at constructing a decision model of an integrated multi-site production scheduling problem. To support multi-site factories with their mass orders, based on the premise that they were under total order management systems, the decision model considered such complicated factors as the product market features, due date, production scheduling, and order profit and capacity of each site. Most real-world scheduling problems involve multiple objectives which may be conflicting with each other. In addition, the effect factors taken into account by previous multi-objective scheduling research are essentially quantitative factors. However, more qualitative factors also have to be considered related to organizations’ operating messages. We propose an integrated production scheduling model and use hybrid genetic algorithm methods as solution procedures.