A New Approach for Planning and Scheduling Problems in Hybrid Distributed Manufacturing Execution System

In order to acquire the farsighted development, the business enterprises not only need to face the complicated exterior environment, but also need to face the enterprises resources management. Multi-location manufacturing enterprises are being forced into greater collaboration with customers, suppliers and inner enterprise resources in order to produce quality products in smaller batches, shorter lead times and with greater variety. This paper presents a creative approach for solving dynamic planning and scheduling problems in hybrid distributed manufacturing execution system (HDMES). We adopt a facility layout optimization approach based on single genetic algorithm (SGA) and parallel genetic algorithm (PGA). Different approaches used to model various aspects of manufacturing processes are reviewed and found, and an approach based on scheduling rules, multi-agent technique, multi-closed loop control and user cooperation is presented to solve the distributed planning and scheduling problem. We hierarchically decompose the large-scale planning and scheduling problem into the planning level problems, the scheduling level problems and material tracking feedback level problems. In the optimization approach, the genetic operators and selection method are used to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The optimization approach based on genetic algorithm is tested on typical production management problems in hybrid distributed manufacturing execution system. The results are compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement in solution quality. The superior results indicate the successful incorporation of a method to generate initial population into the genetic operators