单基地固定区段动车组乘务交路计划优化编制方法

doi:10.16097/j.cnki.1009-6744.2026.01.014

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 148-160.DOI: 10.16097/j.cnki.1009-6744.2026.01.014

单基地固定区段动车组乘务交路计划优化编制方法

陈维亚^*a,b，叶凤女^a,b，李朵^a,b，袁子越^a,b

中南大学，a.交通运输工程学院；b.轨道交通大数据湖南省重点实验室，长沙410075

收稿日期:2025-10-14 修回日期:2025-11-24 接受日期:2025-12-09 出版日期:2026-02-25 发布日期:2026-02-15
作者简介:陈维亚（1981—），男，湖南桃江人，教授，博士。
基金资助:
中国铁路兰州局集团有限公司科技研究开发计划(202522-1)。

Optimization Method for Single-depot Fixed-route EMU Crew Routing Planning

CHEN Weiya^*a,b, YE Fengnv^a,b, LI Duo^a,b, YUAN Ziyue^a,b

a. School of Traffic & Transportation Engineering; b. Hunan Provincial Key Laboratory of Rail Transit Big Data, Central South University, Changsha 410075, China

Received:2025-10-14 Revised:2025-11-24 Accepted:2025-12-09 Online:2026-02-25 Published:2026-02-15
Supported by:
China Railway Lanzhou Group Co Ltd Science and Technology Research and Development Plan (202522-1)。

摘要/Abstract

摘要： 编制动车组乘务交路计划是高速铁路运输组织的关键技术环节，编制质量直接影响运营乘务成本和乘务员工作效率。针对单基地固定区段动车组乘务交路计划编制问题，提出“少班快转、便乘优先和过夜均衡”优化编制策略，构建兼顾降低运营乘务成本，提高乘务员工作效率及尽可能满足乘务员工作偏好的多目标两阶段优化模型和算法。第1阶段，实施“少班快转”优化策略，构建以最大化乘务区段接续数量和最小化乘务区段总接续时间为双层优化目标的数学模型，设计融合基于帕累托前沿的信息素增量分配策略和混合精英策略的改进蚁群算法，求解获得乘务员数量最少的初始乘务区段接续组合；第2阶段，实施“便乘优先和过夜均衡”优化策略，以第1阶段的优化结果为基础，建立以最小化便乘和异地过夜补贴总费用为优化目标的数学模型，设计启发式算法求解，获得综合最优的乘务交路计划。以兰州局管辖的徐兰高铁动车组开行方案数据为实例，测试模型和算法，求解结果验证了所提出方法能快速求出动车组列车成对开行和非成对开行情形下的乘务交路计划。所提出的优化策略和编制方法可为优化动车组乘务调度提供兼顾经济效益与人员满意度的决策支持，对同类资源优化调度问题也具有参考价值。

关键词: 铁路运输, 乘务交路计划, 多目标两阶段优化, 固定区段轮乘制, 改进蚁群算法

Abstract: Formulating crew routing plans for electric multiple units (EMU) constitutes a critical operational procedure in high speed railway management. Its execution quality directly impacts both crew operation costs and crew member productivity. To address the planning challenge of single-depot fixed-route EMU crew routing, this study proposes an optimization strategy featuring "shift consolidation with rapid transfer, deadhead priority, and balanced overnight stays". An optimization model and algorithm with multi-objective two-stage is developed to simultaneously reduce operational costs, enhance crew productivity, and accommodate crew work preferences as much as possible. Stage 1 implements the "shift consolidation with rapid transfer" strategy. Abi-level optimization model is constructed with the upper level maximizing crew duty section connections and the lower level minimizing total connection time. An enhanced Ant Colony Algorithm is designed, which incorporates a pheromone increment allocation strategy based on the Pareto front and a hybrid elite strategy. This stage yields an initial set of crew duty section connections with the minimum required crew size. Stage 2 executes the "deadhead priority and balanced overnight stays" strategy based on Stage 1 results. A mathematical model is established to minimize deadhead travel and overnight accommodation subsidies at remote locations. A heuristic algorithm is designed to obtain the comprehensively optimized crew routing plan. Using operational data from the Xuzhou-Lanzhou High-speed Railway under the Lanzhou Railway Bureau as a case study, the model and algorithm were tested. Results demonstrate that the proposed method efficiently generates crew routing plans for both paired and unpaired train services. The optimization strategies and planning methodology provide decision support for EMU crew scheduling that balances economic efficiency and personnel satisfaction, and also offers reference value for similar resource scheduling optimization problems.

Key words: railway transportation, crew routing planning, multi-objective two-stage optimization, fixed-route crew rotation system, enhanced Ant Colony Algorithm

中图分类号:

U292.6

陈维亚, 叶凤女, 李朵, 袁子越. 单基地固定区段动车组乘务交路计划优化编制方法[J]. 交通运输系统工程与信息, 2026, 26(1): 148-160.

CHEN Weiya, YE Fengnv, LI Duo, YUAN Ziyue. Optimization Method for Single-depot Fixed-route EMU Crew Routing Planning[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(1): 148-160.

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单基地固定区段动车组乘务交路计划优化编制方法

Optimization Method for Single-depot Fixed-route EMU Crew Routing Planning

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