交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 161-171.DOI: 10.16097/j.cnki.1009-6744.2026.01.015

• 系统工程理论与方法 • 上一篇    下一篇

可预知任务变动下的高铁乘务排班计划优化

钟文健1，李想1，高政2，卢迪燊3，林柏梁*1   

  1. 1. 北京交通大学，交通运输学院，北京100044；2.中国铁路济南局集团有限公司，青岛动车段，山东青岛266000；3. 新南威尔士大学，计算机科学与工程学院，悉尼2052，澳大利亚
  • 收稿日期:2025-09-12 修回日期:2025-10-29 接受日期:2025-11-04 出版日期:2026-02-25 发布日期:2026-02-15
  • 作者简介:钟文健（1999—），男，浙江宁波人，博士生。
  • 基金资助:
    国家自然科学基金铁路基础研究联合基金(U2268207)。

Crew Rostering Optimization for High-speed Railways Under Predictable Task Changes

ZHONG Wenjian1, LI Xiang1, GAO Zheng2, LU Dishen3, LIN Boliang*1   

  1. 1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2. Qingdao EMU Depot, China Railway Jinan Group Co Ltd, Qingdao 266000, Shandong, China; 3. School of Computer Science and Engineering, University of New South Wales, Sydney 2052,Australia
  • Received:2025-09-12 Revised:2025-10-29 Accepted:2025-11-04 Online:2026-02-25 Published:2026-02-15
  • Supported by:
    National Nature Science Foundation of China(U2268207)。

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摘要： 随车机械师日常运用中存在大量加开交路、学习、请假等可预知的任务变动，导致乘务排班计划频繁调整。然而，传统方法的调整能力较弱，难以满足计划稳定性和工作量均衡的需求。因此，本文针对随车机械师的工作特性，构建乘务排班计划优化模型。该模型以任务调整数量最小为主要优化目标，以工作量均衡为次要目标，并引入工时上限、大休安排、多日任务接续等多种约束条件以确保计划的可行性和合理性。此外，模型还针对异地机械师调度问题进行扩展，考虑便乘时间对任务安排的影响。通过线性化技术，将模型转化为线性的0-1规划问题。案例基于青岛北动车所真实数据进行研究，并利用商业求解器GUROBI求解。结果表明：当发生36次可预知任务变动时，本文模型仅引发32次任务调整，而传统方法则需352次，调整次数仅为传统方法的9.09%；同时，模型将随车机械师工作量的最大偏差控制在单个值乘任务范围内，为1645，仅为传统方法的38.7%。

关键词: 铁路运输, 乘务排班计划, 线性化, 随车机械师, 可预知任务变动

Abstract: In the daily operations of onboard mechanics, there are numerous predictable task changes, such as additional tasks, training, and leave requests, which lead to frequent adjustments to the crew rostering plan. However, the adjustment capability of traditional method is limited, which means it difficult to meet the requirements of plan stability and workload balance. Therefore, this paper focuses on the work characteristics of onboard mechanics, and then constructs an optimization model. The model prioritizes the minimum changes to the original task arrangement during plan adjustments, with workload balance as a secondary objective. The constraints, including the upper limit of working hours, scheduling of big breaks, and the continuity of multi-day tasks, are incorporated to ensure the feasibility and rationality of plan. In addition, the model is extended to address the scheduling of out-of-town mechanics, taking into account the impact of travel time on task assignments. Through the linearization technique, the model is transformed into a linear 0-1 planning problem. A case study is conducted based on the real data from the Qingdao North Depot, and solved by using the commercial solver GUROBI. The results show that when 36 predictable task changes occur, the model triggers only 32 task adjustments, whereas the traditional method requires 352 adjustments, meaning that the number of adjustments under the model accounts for only 9.09% of that required by the traditional method. At the same time, the model limits the maximum deviation of the workload of onboard mechanics to a single duty task, at 1 645, which is only 38.7% of that under the traditional method.

Key words: railway transportation, crew rostering, linearization, onboard mechanic, predictable task change

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