Modeling Of Multi-resource Production Scheduling Problem

The production scheduling is the base of production system, optimizing technology of production scheduling, is the key to advanced technology of the production and management. Most researches of production scheduling are focused on single-resource production scheduling problem. However, in practical production system, many scheduling problems are dual-resource and multi-resource production scheduling that more and more researchers have focused on in recent years. Therefore, the research of this domain is significant.This dissertation tries to address the problem of dual-resource and multi-resource production scheduling. In the case of dual-resource production scheduling, we propose a model with the restricted resources of machines and workers. We exploit hybrid genetic algorithm (HGA) to address the scheduling problem. In the case of multi-resource production scheduling, the system is the restricted resources by machines, workers and robots, which is added robots compared with the dual-resource production scheduling. In practical system, robots can be displaced by transports or other equipments. In these years, genetic algorithm (GA) and simulated annealing (SA) have been used to address the problem of production scheduling and achieve a good performance in complex production scheduling problems. But, GA and SA both have limitations. HGA is the combination of genetic algorithm and simulated annealing. HGA has the merits of GA and SA, and averts the demerits of them. So HGA has better performance than standard genetic algorithm in solving production scheduling problem. In this dissertation, we exploit HGA to solve dual-resource production scheduling and multi-resource production scheduling problem. At last we implement and experimentally evaluate our approach.