基于粒子群算法的流程工业生产调度研究

以优化流程工业生产为目标,研究了将基于惯性权重的粒子群算法应用到流程工业的生产调度问题。在对流程工业生产调度问题进行分析的基础上,建立了以总加工完成时间最短为优化目标的生产调度模型。调度算法采用动态惯性权重,使惯性权值在粒子群算法搜索过程中线性变化,以提高粒子群算法的优化性能。给出了粒子编码与解码实现方法,以及具体的算法实现过程。以某流程工业企业生产调度实例为例,利用建立的优化调度模型和设计的粒子群算法进行了实验仿真,结果表明,建立的调度模型和设计的算法是可行的,与蚁群系统方法相比较,有较好的调度性能,适用于解决流程工业实际生产调度问题。

Research on production scheduling problems in process industry basedon particle swarm optimization algorithm.

In order to improve the production of process industry, Particle Swarm Optimization （PSO） algorithm based on inertia weight is applied to production scheduling problem. Based on the analysis of the production scheduling problem for process industry, a production scheduling model is established, whose goal is to obtain the shortest total process time. The dynamic inertia weight is introduced into the basic PSO algorithm to improve its performance, which the inertia values are changed during the optimization algorithm searching. The coding and decoding of optimization algorithm, the detail algorithm implementation are also discussed. Using a practical production scheduling problem as an example, the established model and designed algorithm are applied to implement scheduling simulation. The simulation results show that the scheduling model and algorithm are feasible, and have a better scheduling performance than ant colony system scheduling, and can be aoolied to solve oractical oroduction schedulinR oroblem for process industry.