Decision-making strategies for decentralized production planning and scheduling

Decision-making strategies for production planning and scheduling problems in production systems which consist of several plants are studied. First, the authors formalize a way of representing a problem by introducing two kinds of models: a physical model and an information model. The physical model is used for simulating the process of manufacturing activities, while the information model is used for a decision making on production planning and scheduling. In designing the information model, an autonomous decentralized approach is adopted and some typical structures of the model are reformulated. Then, the genetic algorithm is implemented to solve the problems using the information model. Through some computational examples with some simple production planning problems, the possibility and the effectiveness of the proposed approach are investigated.