基于改进遗传算法的多目标柔性作业车间节能调度问题  

Multi-objective flexible job shop energy-saving scheduling problem based on improved genetic algorithm

作　　者：王雷[1] 蔡劲草 石鑫[1] 

机构地区：[1]安徽工程大学机械与汽车工程学院,安徽芜湖241000

出　　处：《南京理工大学学报》2017年第4期494-502,共9页Journal of Nanjing University of Science and Technology

基　　金：国家自然科学基金(51305001);安徽省自然科学基金(1708085ME129);安徽省高校优秀青年人才支持计划重点项目(gxyq ZD2016125);安徽省科技计划项目(1604a0902183)

摘　　要：为降低柔性作业车间调度中的能耗,在分析柔性作业车间调度问题研究现状和不足的基础上,以完工时间、机器能耗和工人操作机器的舒适度作为柔性作业车间调度问题的多目标函数。利用改进遗传算法对其进行优化研究。算法中采用权重法对种群进行初始化处理以获得较好的解;采用快速解码获得需要的总适应度值;利用改进的交叉及变异操作,避免非法解的产生;利用精英保留策略保留优秀基因,提高求解效率和求解质量。通过对具体案例仿真验证算法的有效性。To reduce the energy consumption in flexible job shop scheduling, by analyzing the current research status and insufficiency, the makespan, power consumption of machine and the comfort level of employee are supposed as multi -objectives function for flexible job shop scheduling problem （FJSP） . An improved genetic algorithm is proposed to optimize this problem. The weighting method is used to initialize the population in order to obtain better solution, meanwhile the total fitness value is obtained by a fast decoding method. The modified crossover and mutation operations are used to avoid creating the illegal solution. The elitism strategy is used to keep good genes. The efficiency andquality of solution can be improved by using the proposed improved genetic algorithm. Simulation tests are done to verify the effectiveness of the proposed improved genetic algorithm.

关 键 词：改进遗传算法 多目标 柔性作业车间调度 舒适度 节能调度 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]