平行机系统生产调度与维护计划联合优化  

Integrated Preventive Maintenance Planning and Production Scheduling on Parallel Machine

作　　者：黄方明[1] 陆志强[2] 崔维伟[3] 

机构地区：[1]上海交通大学中美物流研究院,上海200030 [2]同济大学机械与能源工程学院,上海201804 [3]上海交通大学机械与动力工程学院,上海200240

出　　处：《工业工程与管理》2013年第4期49-55,共7页Industrial Engineering and Management

基　　金：国家自然科学基金资助项目(71171130;50905115)

摘　　要：针对平行机系统中生产调度和维护计划的联合决策问题,假设随机故障服从威布尔分布,将作业在设备上加工位置以及设备上预防性维护位置作为决策变量,以最小化最大完工时间和最小化单位维护成本作为优化目标建立了多目标优化模型。建立了基于混合编码的遗传算法,针对不同编码类型采用合适的遗传算子,并引入了自适应交叉和变异概率使算法在收敛速度和求解精度上得到较好平衡。通过与枚举算法对比,证明遗传算法具有较好的时间效率和求解精度。通过与独立决策模型对比,证明联合优化模型能更好地解决联合优化问题,提高企业整体效益。This paper deals with the joint production and maintenance scheduling problem which takes Weibull distributed stochastic breakdowns into account. The objective is to decide the sequence of n jobs and assignment of preventive maintenance（PM）actions on m machines, which minimizes the makespan and the average cost simultaneously. A revised genetic algorithm with hybrid coding is proposed, and best genetic operator is utilized for different type of coding. By applying adaptive probabilities of crossover and mutation, the GA converges to the global optimum in fewer generations and it gets stuck at a local optimum fewer times. Comparing with numeration algorithm shows GA ~s efficiency and accuracy. A comparison with Separated Decision-making model shows that Joint Decision-making mode can better solve this integrated optimization problem and improve the overall goals.

关 键 词：平行机 生产调度 预防性维护 多目标优化 遗传算法 

分 类 号：F224[经济管理—国民经济]