基于遗传算法的动车运用所一级检修作业计划优化  

Optimization of First-Level Maintenance Plan in EMU Depot based on Genetic Algorithm

作　　者：童佳楠 聂磊[1] 贺振欢[1] 

机构地区：[1]北京交通大学交通运输学院,北京100044

出　　处：《铁道运输与经济》2016年第8期59-65,共7页Railway Transport and Economy

基　　金：国家自然科学基金项目(U1434207);中国铁路总公司科技研究开发计划课题(2013X014-C);中华人民共和国交通运输部交通运输科研经费研究项目(2015-2-3)

摘　　要：在动车运用所的洗车线、检修线等固定设备已定的前提下,合理安排动车组一级检修作业计划对提高动车运用所的检修能力具有重要意义。动车组由于长短编组不同,在检修时对双列位检修线的占用情况也不同。以同一列位在同一时间最多只能被1列动车组占用的时空相容性及动车组运用计划为约束条件,以最后1列动车组检修完成时间最小为目标,建立动车所一级检修作业计划优化模型,将问题转化为带特殊工艺约束的混合flow-shop调度问题,并采用自适应的遗传算法进行求解。最后以某动车运用所为例,验证模型和算法的可行性及有效性。Under the preconditions of fixed equipments like washing siding and maintenance tracks in EMU depot having been determined, reasonable arrangement of first-level EMU maintenance plan has an important significance for increasing the depot＇s maintenance capacity. As each maintenance track can be occupied by two short-formation EMUs, EMUs with different formation occupy the maintenance track in different ways. Taking the space compatibility and the EMU operation plan as the constraints, and taking minimization of the last EMU＇s completion time as the object, the optimization model of first-level maintenance plan in EMU depot is established, and then, the original problem is transformed into a hybrid flow-shop scheduling problem with special process constraints, and self-adaptive genetic algorithm is used to solve the model. In the end, taking an EMU depot as an example, this paper validates the feasibility and effectiveness of the model and algorithm.

关 键 词：动车运用所 一级检修 混合flow-shop 遗传算法 

分 类 号：U266.2[机械工程—车辆工程] U269[交通运输工程—载运工具运用工程]