基于延迟时间的地铁车辆多级维修计划优化模型  被引量:6

Optimum Multi-level Maintenance Scheduling Model for Metro Vehicles with Delay Time Theory

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作  者:龙翔宇 刘葛辉 陈绍宽[1] 彭宏勤[1] 刘爽[1] LONG Xiangyu;LIU Gehui;CHEN Shaokuan;PENG Hongqin;LIU Shuang(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University,Beijing 100044,China;CHELBI Engineering Consultants,Inc.,Beijing 100029,China)

机构地区:[1]北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室,北京100044 [2]华杰工程咨询有限公司,北京100029

出  处:《铁道学报》2021年第2期30-36,共7页Journal of the China Railway Society

基  金:国家自然科学基金(71571015,71621001)。

摘  要:现行的地铁车辆维修制度以固定周期维修为主,存在停机时间长、考虑不同子系统的维修需求不足等问题。基于延迟时间理论,以低级维修周期和高级维修可靠度阈值为决策变量、优化更新周期内平均费用为目标,建立车辆系统内单一子系统多级维修计划优化模型,并给出基于实际数据的退化参数估计方法;以整个车辆系统为研究对象,考虑各个子系统间的经济关联性,提出基于子系统退化函数的维修活动调整惩罚函数,建立最大化全局收益的成组维修优化模型,并改进基于滚动时域和动态规划的求解算法。案例研究表明,本文模型可有效减少维修停时、节省维修成本,对运营企业制定维修计划具有一定的指导意义。Current maintenance schedules for urban rail transit vehicles with fixed periods take long downtime and neglect various maintenance requirements from different subsystems or components.A scheduling model involving multi-level maintenance activities based on delay time theory was introduced firstly for a subsystem to minimize the average cost by optimizing the low-level maintenance cycle and reliability threshold for high-level maintenance.The estimation method on degradation parameters was subsequently illustrated based on the actual data.Taking the entire vehicle system as the research object,considering the economic correlation among subsystems,an optimization model for system schedule was further developed for the maximum profit cost considering cost reduction due to grouping maintenance and penalty cost from subsystems.An improved rolling horizon method with dynamic programming was applied to solve the proposed model.The case study validates that the proposed model is able to reduce the downtime and cost for maintenance and provide a guidance for maintenance schedule preparation in practice.

关 键 词:地铁列车车辆 延迟时间 多级维修 成组维修 优化模型 

分 类 号:U279[机械工程—车辆工程]

 

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