工业工程

工业工程 ›› 2022, Vol. 25 ›› Issue (4): 158-164.doi: 10.3969/j.issn.1007-7375.2022.04.019

• 实践与应用 • 上一篇    下一篇

铁路勘测外业的送接车辆调度优化问题

曾俊豪1, 邓连波2   

  1. 1. 中铁四院集团南宁勘察设计院有限公司，广西 南宁 530003;
    2. 中南大学 交通运输工程学院，湖南 长沙 410075
  • 收稿日期:2021-03-31 发布日期:2022-08-30
  • 作者简介:曾俊豪(1991—)，男，广西壮族自治区人，助理工程师，硕士，主要研究方向为铁路勘测设计及运输组织优化
  • 基金资助:
    国家自然科学基金资助项目(71871226)

A Research on Optimization of Railway Survey Vehicle Scheduling

ZENG Junhao1, DENG Lianbo2   

  1. 1. Nanning Survey and Design Institute Co., Ltd, China Railway Siyuan Group, Nanning 530003, China;
    2. School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
  • Received:2021-03-31 Published:2022-08-30

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摘要： 针对目前铁路勘测车辆调度方式主要为司机响应勘测人员实现送、接，导致整体等待时间较长的现状，研究如何构建合理的车辆调度优化模型，实现工作量分配与广义勘测成本协同优化，为勘测工作提供最优的运输组织保障。根据勘测车辆行驶特征，使用送–接扩展网络描述勘测车辆调度优化问题，在此基础上，兼顾决策者和司机的利益目标建立车辆调度双层规划模型。其中，上层模型为广义勘测成本分配最均衡和最大车辆送–接用时最小化模型，下层模型为求解最小广义勘测成本的多车送–接车辆路径模型。建立嵌套遗传算法求解模型。实例分析表明，基于双层规划模型优化所得广义勘测成本相较于目前采用的响应式调度方式减少18%，验证了模型与算法的有效性。

关键词: 铁路勘测, 车辆调度, 双层规划, 遗传算法

Abstract: In view of the current situation that the predominant method of railway survey vehicle scheduling is that the driver responds to the surveyors to realize picking and sending, which leads to a long waiting time. A study is conducted on how to build a reasonable vehicle scheduling optimization model to realize the balance and collaborative optimization of workload distribution and to generalize survey cost for the sake of providing the optimal transportation organization guarantee for the survey work. According to the traveling characteristics of survey vehicles, the extended network of picking and sending is used to describe the optimization problem of survey vehicle scheduling. On this basis, the bi-level programming model of vehicle scheduling is established considering the interests of decision makers and drivers. The upper model ensures the most balanced distribution of generalized survey cost, and the lower one solves the multi-vehicle routing model with picking and sending. The case study shows that the generalized survey cost based on the bi-level programming model of survey vehicle scheduling is reduced by 18.0% compared with the current reactive scheduling method, which verifies the effectiveness of the model and algorithm.

Key words: railway survey, vehicle scheduling, bi-level programming, genetic algorithm

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