双资源作业车间双目标调度优化研究  

Research on Bi-objective Scheduling Optimization for DRC Job Shop

作　　者：刘晓霞[1] 蔡刚毅[1] 谢里阳[2] 

机构地区：[1]河南工业大学机电工程学院,郑州450007 [2]东北大学机械工程及自动化学院,沈阳110004

出　　处：《组合机床与自动化加工技术》2009年第10期107-112,共6页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金资助项目(50275025)

摘　　要：考虑在制品库存费用、机床工时费、直接工人的工资费用、工件的提前和拖期完工造成的损失费用,提出了一种双资源作业车间调度的生产费用计算方法。将一种新的排序策略嵌入到Pareto竞争方法中,设计了一种新的混合遗传算法,对生产周期和生产费用两个目标同时进行优化。为了保证解的多样性,采用小生境技术并同时使用多种交叉方法。用Pareto解集过滤器保存进化过程中的最优个体并不断更新,防止最优解的遗失。算法最后给出问题的Pareto最优解集。仿真结果表明:该方法是可行的,并具有一定的优越性。Cost of jobs-in-process inventory, machining cost, worker cost and cost of inventory for finishing jobs too early or cost of tardiness penalty are considered, a method calculating production cost of dual-resource job shop scheduling is proposed. A new hybrid genetic algorithm is designed by combining a kind of new ranking strategy with Pareto competition method. In order to promote solution diversity, the niche technology and four kinds of crossover operations are used here. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. The set of Pareto optimum solutions is obtained. In the end, a simulation experiment is carried out to illustrate that the proposed method is feasible and efficient.

关 键 词：双目标优化 双资源作业车间调度 生产费用 混合遗传算法 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置]