混合遗传算法在柔性系统动态调度中的应用研究

本文研究了柔性制造系统实时生产环境下的动态调度问题.提出了基于动态数据库技术的动态调度系统的框架结构.动态数据库中存储着问题的数据结构,包含工件相关类与机器相关类信息.动态数据库能够随着生产的进行及时进行更新.扰动发生后,遗传算法根据动态数据库所提供的更新后的调度任务数据,快速产生新的优化调度方案.通过在遗传算法中嵌入约束解决机制确保遗传算法适应约束的能力,从而提高算法的收敛速度与精度.仿真实验证实了方案的有效性.

A HYBRID GENETIC ALGORITHM APPROACH TO THE DYNAMIC SCHEDULING IN FMS

The FMS scheduling task requires scheduling a set of jobs on a finite set of resources according to the production plans to optimize some given objectives. The jobs to be scheduled together with machines and other resources are often taken as to be deterministic throughout the entire planning horizon. This paper studies the FMS real time dynamic scheduling problem. The proposed dynamic scheduling system consists of five modules. They are dynamic database management module, hybrid genetic algorithm module, new plan download module, static database module, and knowledge base module. Dynamic database stores the data structure of the problem including the information related to parts and machines. After disturbance occurs, dynamic database can update the scheduling task data quickly according to the current production status of the system. The algorithm module is the core of the dynamic scheduling system. It directly determines the response time of the system to the disturbance. Genetic algorithm is used here to generate initial schedule as well as new ones. Static database mainly contains GA parameters and the GA approximate scheduling computation time. The knowledge base stores a great deal of heuristic rules, system status information, and the knowledge representation between them. It is used to provide suitable simulation rules for the GA algorithm.