基于遗传算法的港口设备事后维修的单机调度  

Corrective maintenance scheduling for single type of port machine based on genetic algorithm

作　　者：陈晨 林丹萍 苌道方 Chen Chen;Lin Danping;Chang Daofang(Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China;Logistics Engineering College,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学物流科学与工程研究院,上海201306 [2]上海海事大学物流工程学院,上海201306

出　　处：《计算机应用研究》2019年第9期2590-2595,共6页Application Research of Computers

基　　金：上海市浦江人才计划资助项目(15PJ1402800);国家自然科学研究基金资助项目(71701126)

摘　　要：针对港口设备在损坏后的维修调度问题,即事后维修的调度问题,通过对港口设备的事后维修调度安排进行分析,建立维修设备的调度模型。模型中使用BP神经网络算法来量化港口待维修设备的权值,并利用遗传算法来最小化维修作业任务的总加权完成时间,获得优化后的维修调度顺序和相对应的维修时间安排。通过港口吊具设备的维修算例,展示了优化的调度模型在港机设备中的运用,模型明确了港机的维修顺序,并在保证维修任务完成的情况下节约了维修时间,为港口设备维修计划提供参考。For the problem of the port equipment maintenance scheduling when it was broken,i. e. corrective maintenance,the maintenance model of port equipment was established through analyzing the dispatching schedule of port equipment. In the model,the neural network algorithm was used to quantify the weight of the equipment to be repaired in the port,and the genetic algorithm was used to minimize the total weighted completion time of the maintenance task and to obtain the optimized maintenance scheduling sequence and the corresponding maintenance schedule. A practical example of port equipment was presented to show the corrective maintenance model was optimized. The results show that the proposed model saves maintenance time as well as fulfills the maintenance task. The proposed model will provide a valuable reference for planning the port equipment maintenance.

关 键 词：港口设备 事后维修 BP神经网络 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]