考虑多目标的柔性工艺与调度集成优化算法  

Integrated optimization algorithm of flexible process planning and shop scheduling with consideration of multi-objectives

作　　者：宋栓军[1] 杨佩莉 石雯丽[1] 

机构地区：[1]西安工程大学机电学院,西安710048

出　　处：《计算机应用研究》2017年第7期1980-1984,1988,共6页Application Research of Computers

基　　金：陕西省教育厅科研基金资助项目(15JK1311);西安工程大学博士科研启动基金资助项目(BS1301);西安工程大学研究生创新基金资助项目(CX201628)

摘　　要：针对工艺规划与车间调度集成优化问题,在考虑零件的加工工序柔性、工序次序柔性及加工机器柔性的基础上,以最大完工时间、总加工成本和总拖期时间为优化目标,对多目标柔性工艺与车间调度集成问题建模,提出一种基于改进人工蜂群算法的多目标柔性工艺与车间调度集成优化策略,并提出邻域变异操作以及全局交叉操作,对种群进行更新。引入Pareto方法,通过对适应度评价、贪婪准则、Pareto最优解集构造和保存以及解得多样性维护等方面进行改进,设计了一种基于Pareto方法的多目标人工蜂群算法。通过采用基本人工蜂群算法及改进人工蜂群算法对六个工件、五台机床的柔性工艺与车间调度集成问题进行优化,验证了改进算法的有效性。Aiming at the problem of the integration and optimization of process planning and scheduling, in considering flexibility of the manufacturing procedure and order and machine of the technics, with the greatest completed time and the total processing cost and total tardiness time as the optimization object, modeled the integrated multi-objective flexible process and scheduling, this paper proposed an improved artificial bee colony algorithm for multi-objective flexible process and workshop scheduling integrated optimization strategy, and put forward neighborhood mutation and global crossover operation to updates the population. Introducing Pareto method, based on improving adaptation degree evaluation, greedy criterion, the constructed and saved of the Pareto optimal solution set and solution diversity maintenance, it proposed a method of multi-objective artificial bee colony algorithm based on Pareto. Finally, using the basic artificial bee colony algorithm and improved artificial bee colony algorithm of six workpieces, five machines flexible process and Job-Shop scheduling problem of integration optimization verify the effectiveness of the improved algorithm.

关 键 词：人工蜂群算法 柔性工艺 车间调度 集成 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置] TP301.6[自动化与计算机技术—控制科学与工程]