用于柔性流程工业生产调度的并行多目标遗传算法

分析了柔性流程工业区别于一般制造系统在生产调度方面的特点 ,在此基础上针对这类系统所具有的多目标、混合动力学特性、调度实时性等特征 ,提出了一种新的并行多目标遗传算法 .在解决多目标解的评价时 ,采用了目标分级评价技术 ,求解过程始终将解的最优性和决策者对目标的偏好信息结合在一起 .为反映这类问题的混合特性 ,提出了一种新的双层编码方案 .在算法中采用的递阶分解并行技术 ,使计算时间至少减少一个数量级 .计算机仿真结果表明 ,对于这一类复杂的柔性流程工业生产调度优化问题 ,本文提出的方法具有很好的实际应用前景 .

A Novel Parallel Multi-Objective Genetic Algorithm for Process Industry Production Scheduling

This paper analyzes the features of the production scheduling problems in process industries which are different from general manufacturing systems. A parallel multi-objective genetic algorithm is proposed based on the distinctive characteristics of the process scheduling, such as multiple objectives, hybrid dynamics and real time computation. An objective ranking evaluation technique is developed to associate the tradeoff information to a better solution with preference articulation. A novel double-layer chromosome coding method is used to express the system hybridness. Computation time is at least reduced to 10 percent of its original value by adopting a hierarchical decomposed parallel computing technique. Simulation results show that the algorithm illustrated in this paper has a prospective future in the applications to such complex optimization problems in process scheduling.