基于人机协同决策的运输调度优化研究

doi:10.16097/j.cnki.1009-6744.2024.02.014

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (2): 136-148.DOI: 10.16097/j.cnki.1009-6744.2024.02.014

基于人机协同决策的运输调度优化研究

刘涛^*1a,1b,1c,2，尤海林^1a,1c

1. 西南交通大学，a.交通运输与物流学院，b.综合交通大数据应用技术国家工程实验室， c. 系统科学与工程研究院，成都611756；2.宜宾西南交通大学研究院，四川宜宾644000

收稿日期:2023-11-30 修回日期:2024-01-27 接受日期:2024-02-04 出版日期:2024-04-25 发布日期:2024-04-25
作者简介:刘涛(1989- )，男，四川人，副教授，博士。
基金资助:
国家自然科学基金 (72271206, 61903311)； “双城”市校协议专项科研经费项目(YBSCXY2023020009)。

Human-machine Collaborative Decision-making for Transportation Scheduling Optimization

LIU Tao^*1a,1b,1c,2,YOU Hailin^1a,1c

1a. School of Transportation and Logistics, 1b. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, 1c. Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu 611756, China; 2. Yibin Research Institute of Southwest Jiaotong University, Yibin 644000, Sichuan, China

Received:2023-11-30 Revised:2024-01-27 Accepted:2024-02-04 Online:2024-04-25 Published:2024-04-25
Supported by:
NationalNaturalScienceFoundation of China (72271206, 61903311)；Twin-city University and CityAgreed Special Scientific Research Funding (YBSCXY2023020009)。

摘要/Abstract

摘要： 为求解带柔性约束的运输调度问题，本文提出一个基于逆差函数的人机协同决策方法。该方法主要包括两个阶段，第1阶段，借助数学规划模型，利用计算机的强大计算能力，快速求解得到问题的一个可行解；第2阶段，借助逆差函数模型，利用人类自身的知识与经验，在考虑柔性约束的情形下，进一步优化第1阶段的可行解。两个阶段通过逆差函数图像构成的图形用户界面进行实时交互，实现人机协同决策。通过网约定制公交车辆调度和民航飞机调度两个案例问题的求解，验证基于逆差函数的人机协同决策方法的有效性。计算结果表明：该方法可实现车次链/航班链的自动化构建和空驶车次的自动化插入，有助于求解带有复杂柔性约束的运输调度问题。

关键词: 系统工程, 人机协同决策, 逆差函数, 运输调度, 交互式优化

Abstract: The paper proposes a human-machine collaborative decision-making methodology based on the deficit function model to solve transportation scheduling problems with flexible constraints. The methodology mainly consists of two stages. In the first stage, by making use of mathematical programming models and the powerful computing capacity of computers, a feasible solution is quickly obtained. In the second stage, with the help of the deficit function model, human beings' own knowledge and experience are employed to further optimize the feasible solution obtained in the first stage, while taking into account flexible constraints. The two stages interact in real time through a graphical user interface composed of deficit function figures, thereby realizing human-machine collaborative decision-making. The effectiveness of the proposed human-machine collaborative decision-making methodology based on the deficit function model is demonstrated through two case studies, i.e., app-based customized bus scheduling problem and civil aviation aircraft scheduling problem. Computation results show that the proposed methodology can realize the automatic construction of vehicle/flight chains and the automatic insertion of deadheading trips. It is useful for solving complex transportation scheduling problems with flexible constraints.

Key words: system engineering, human-machine collaborative decision-making, deficit function, transportation scheduling, interactive optimization

中图分类号:

N945

刘涛, 尤海林. 基于人机协同决策的运输调度优化研究[J]. 交通运输系统工程与信息, 2024, 24(2): 136-148.

LIU Tao, YOU Hailin. Human-machine Collaborative Decision-making for Transportation Scheduling Optimization[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(2): 136-148.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2024.02.014

http://www.tseit.org.cn/CN/Y2024/V24/I2/136

参考文献

[1]钱学森,于景元,戴汝为.一个科学新领域:开放的复杂巨系统及其方法论[J].自然杂志,1990,13(1): 3-10. [QIAN X S, YU J Y, DAI R W. A new discipline of science: The study of open complex giant system and its methodology[J]. Chinese Journal of Nature, 1990, 13 (1): 3-10.]

[2]王红卫,李珏,刘建国,等.人机融合复杂社会系统研究[J]. 中国管理科学,2023, 31(7): 1-21. [WANG H W, LI J, LIU J G, et al. Research on human-machine integration complex social system[J]. Chinese Journal of Management Science, 2023, 31(7): 1-21.]

[3]曾大军,张柱,梁嘉琦,等.机器行为与人机协同决策理论和方法[J]. 管理科学,2021, 34(6): 55-59. [ZENG D J, ZHANG Z, LIANG J Q, et al. Machine behavior and human-machine collaboration decision: Theory and methods[J]. Journal of Management Science, 2021, 34(6): 55-59.]

[4]国家自然科学基金委员会管理科学部.管理科学发展战略:管理科学“十四五”优先资助领域[M].北京:科学出版社, 2023. [Department of Management Science, National Natural Science Foundation of China. Management science development strategy: Priority funding areas of the "14th five-year plan" for management science[M]. Beijing: China Science Publishing & Media Ltd, 2023.]

[5] LIU T, CEDER A, MA J, et al. Graphical human machine interactive approach for integrated bus transit scheduling: Lessons gained from a large bus company[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(4): 1023-1028.

[6]ZHU W, ZHANG T, YING Z, et al. Real-time high-speed train rescheduling based on a human-computer interaction framework[J]. High- Speed Railway, 2023, 1 (2): 130-140.

[7]YAHOUNI Z, MEBARKI N, BELKADI F, et al. Human machine cooperation in planning and scheduling: A case study on an unstable environment[J]. European Journal of Industrial Engineering, 2018, 12(6): 757-780.

[8]LINIS V K, MAKSIM M S. On the problem of constructing routes[J]. Proceedings of the Institute of Civil Aviation Engineering, 1967, 102: 36-45.

[9] BUNTE S, KLIEWER N. An overview on vehicle scheduling models[J]. Public Transport, 2009, 1(4): 299 317.

[10] 巫威眺, 林越,李余,等.随机行程时间的电动公交调度模型[J]. 中国公路学报,2023, 36(6): 235-253. [WU W T, LIN Y, LI Y, et al. Electric bus scheduling model with stochastic travel time[J]. China Journal of Highway and Transport, 2023, 36(6): 235-253.]

[11] LEVIN A. Scheduling and fleet routing models for transportation systems[J]. Transportation Science, 1971, 5 (3): 232-255.

[12] FORD J L R, FULKERSON D R. Flows in networks[M]. Princeton, NJ, USA: Princeton University Press, 1962.

[13] 徐猛, 刘涛, 钟绍鹏,等.城市智慧公交研究综述与展望[J]. 交通运输系统工程与信息,2022,22(2): 91-108. [XU M, LIU T, ZHONG S P, et al. Urban smart public transport studies: A review and prospect[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 91-108.]

[14] 国务院.《国务院关于印发“十四五”现代综合交通运输体系发展规划的通知》[EB/OL]. (2022-01-18) [2023 11-25]. https://www.gov.cn/zhengce/content/2022-01/18/ content_5669049.htm. [The State Council of the People's Republic of China. Notice on issuing the 14th five-year development plan for the modern comprehensive transportation system. [EB/OL]. (2022-01-18) [2023 11-25]. https://www.gov.cn/zhengce/content/2022-01/18/ content_5669049.htm.]

[1]	彭宏勤, 张国伍. 新时期系统工程与交通运输的理论融合与实践[J]. 交通运输系统工程与信息, 2022, 22(5): 1-6.
[2]	戴庆, 林正奎, 曲毅, 张佳佳. 并列瓶颈路网下个人与家庭混合通勤行为研究[J]. 交通运输系统工程与信息, 2022, 22(1): 182-194.
[3]	王庆云, 毛保华, 张国伍. 我国交通运输系统工程的演化[J]. 交通运输系统工程与信息, 2021, 21(5): 2-11.
[4]	张毅, 姚丹亚, 李力, 裴华鑫, 晏松, 葛经纬. 智能车路协同系统关键技术与应用[J]. 交通运输系统工程与信息, 2021, 21(5): 40-51.
[5]	黄爱玲, 徐笑涵, 关伟, 段梦媛. 基于加权耦合映像格子的地铁网络稳定性演化研究[J]. 交通运输系统工程与信息, 2021, 21(3): 140-149.
[6]	关伟，吴建军，高自友. 交通运输网络系统工程[J]. 交通运输系统工程与信息, 2020, 20(6): 9-21.
[7]	张水潮，蔡逸飞，黄锐，周竹萍. 基于预约需求的共享停车平台泊位分配方法[J]. 交通运输系统工程与信息, 2020, 20(3): 137-143.
[8]	王庆云. 交通发展中的技术及技术含量的判断[J]. 交通运输系统工程与信息, 2019, 19(6): 1-5.
[9]	段力，刘聪健，方炽霖，程紫微. 信号控制与交通分配协同模型的自适应IOA 算法[J]. 交通运输系统工程与信息, 2019, 19(6): 77-84.
[10]	胡宝雨，刘浩，程国柱. 城市公交大站快车与全程车联合调度方法[J]. 交通运输系统工程与信息, 2019, 19(5): 142-149.
[11]	贾文峥，王艳辉，苏宏明，刘悦，林帅. 基于风险网络的轨道交通车辆风险评价[J]. 交通运输系统工程与信息, 2019, 19(4): 143-148.
[12]	许小卫，谢新连，李猛，赵家保. 基于R-THIOWGA集成算子的模糊关联多属性群决策方法[J]. 交通运输系统工程与信息, 2019, 19(1): 6-12.
[13]	王海星，梁倩倩. 危险货物道路运输风险复杂系统及风险测度模型与应用——以液氯运输为例[J]. 交通运输系统工程与信息, 2018, 18(4): 142-147.
[14]	江磊，王小敏，蔺伟. 基于动态贝叶斯网络的列控中心可靠性及可用性评估[J]. 交通运输系统工程与信息, 2018, 18(3): 182-188.
[15]	李岩，周文辉，南斯睿，陈宽民. 基于可靠度的单轨道交通施工区建设方案优化[J]. 交通运输系统工程与信息, 2017, 17(5): 250-256.

基于人机协同决策的运输调度优化研究

Human-machine Collaborative Decision-making for Transportation Scheduling Optimization

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics