考虑驾驶员疲劳的车辆路径优化及算法研究

doi:10.3969/j.issn.1007-7375.2023.02.015

工业工程 ›› 2023, Vol. 26 ›› Issue (2): 132-140,184.doi: 10.3969/j.issn.1007-7375.2023.02.015

考虑驾驶员疲劳的车辆路径优化及算法研究

张树柱, 邱兵兵, 山家骏, 隆清琦

浙江财经大学信息管理与人工智能学院, 浙江杭州 310018

收稿日期:2021-09-26 发布日期:2023-05-05
作者简介:张树柱(1985-),男,山东省人,讲师,博士,主要研究方向为物流优化、供应链管理、智能计算
基金资助:
国家自然科学基金青年资助项目(71902171,72171208,71771195)；教育部人文社科一般资助项目(19YJC630216)；浙江省自然科学基金资助项目(LY19G010003)

Vehicle Routing Optimization and Its Algorithm Considering Driver Fatigue

ZHANG Shuzhu, QIU Bingbing, SHAN Jiajun, LONG Qingqi

School of Information Management and Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China

Received:2021-09-26 Published:2023-05-05

摘要/Abstract

摘要： 针对城市物流中驾驶员面临的高强度工作环境，考虑配送过程中由疲劳引起的驾驶员配送能力的动态变化，以总配送时间最短为目标构建车辆路径优化模型。模型中引入基于疲劳程度的配送速度函数度量驾驶员配送能力的变化，随后结合广度优先与深度优先搜索策略设计一种改进人工蜂群算法求解该模型，最后通过标准测试集与仿真算例验证模型及算法的有效性。实验结果表明，考虑驾驶员疲劳的影响有助于提升驾驶员工作量的均衡性，降低驾驶员的疲劳程度。研究成果对于物流企业配送方案的制定具有重要的参考意义。

关键词: 城市物流, 车辆路径优化, 驾驶员疲劳, 人工蜂群算法

Abstract: In urban logistics, drivers are facing a high-intensity work environment. In view of this, the dynamic changes of drivers’ ability to deliver caused by fatigue is considered in delivery processes, and a vehicle routing optimization model is established with the minimization of total delivery time as the objective. A delivery speed function based on fatigue degree is introduced into the model to quantify the change of drivers' ability to deliver. Then, a modified artificial bee colony algorithm is designed to solve the model integrated with the breadth-first and depth-first search strategies. Finally, the effectiveness of the proposed model and algorithm is verified through standard testing sets and simulation experiments. Experimental results show that considering the impact of driver fatigue is helpful to maintain the balance of drivers' workload and reduce the degree of driver fatigue. The results of this study may have an important reference significance for delivery planning of logistics enterprises.

Key words: urban logistics, vehicle routing optimization, driver fatigue, artificial bee colony algorithm

中图分类号:

U121

张树柱, 邱兵兵, 山家骏, 隆清琦. 考虑驾驶员疲劳的车辆路径优化及算法研究[J]. 工业工程, 2023, 26(2): 132-140,184.

ZHANG Shuzhu, QIU Bingbing, SHAN Jiajun, LONG Qingqi. Vehicle Routing Optimization and Its Algorithm Considering Driver Fatigue[J]. Industrial Engineering Journal, 2023, 26(2): 132-140,184.

参考文献

[1] 国家邮政局. 国家邮政局公布2021年邮政行业运行情况[EB/OL]. [2022-02-13]. http://www.spb.gov.cn/gjyzj/c100276/202201/74c80cf2fd7b44c3aa5d6facb464bcb8.shtml.
[2] 武楷. 快递从业人员权益保障探究[J]. 合作经济与科技, 2021, 648(1): 186-187
WU Kai. Research on the protection of rights and interests of express practitioners[J]. Co-Operative Economy & Science, 2021, 648(1): 186-187
[3] 薛明. 恶劣天气因素下最优车辆路线调度路径选择[J]. 计算机仿真, 2015, 32(3): 210-212
XUE Ming. Optimal path selection of vehicle route scheduling under bad weather factors[J]. Computer Simulation, 2015, 32(3): 210-212
[4] 李妍峰, 高自友, 李军. 基于实时交通信息的城市动态网络车辆路径优化问题[J]. 系统工程理论与实践, 2013, 33(7): 1813-1819
LI Yanfeng, GAO Ziyou, LI Jun. Vehicle routing problem in dynamic urban network with real-time traffic information[J]. Systems Engineering—Theory & Practice, 2013, 33(7): 1813-1819
[5] ZHONG H, HALL R W, DESSOUKY M. Territory planning and vehicle dispatching with driver learning[J]. Transportation Science, 2007, 41(1): 74-89
[6] KUNKEL M, SCHWIND M. Vehicle routing with driver learning for real world CEP problems[C/OL]. (2013-02-09). https://ieeexplore.ieee.org/document/6148681.
[7] ULMER M, NOWAK M, MATTFELD D, et al. Binary driver-customer familiarity in service routing[J]. European Journal of Operational Research, 2020, 286(2): 477-493
[8] SCHNEIDER M, DOPPSTADT C, STENGER A, et al. Ant colony optimization for a stochastic vehicle routing problem with driver learning[C/OL]. (2010-11-27). https://ieeexplore.ieee.org/document/5585999.
[9] SCHNEIDER M. The vehicle-routing problem with time windows and driver-specific times[J]. European Journal of Operational Research, 2016, 250(1): 101-119
[10] GLOCK C H, GROSSE E H, KIM T, et al. An integrated cost and worker fatigue evaluation model of a packaging process[J]. International Journal of Production Economics, 2019, 207: 107-124
[11] GRANOTTO G F, SGARBOSSA F, GLOCK C H, et al. The effect of worker fatigue on the performance of a bucket brigade order picking system[J]. IFAC-PapersOnLine, 2019, 52(13): 2195-2200
[12] GOEL A. Vehicle scheduling and routing with drivers' working hours[J]. Transportation Science, 2009, 43(1): 17-26
[13] 朱子轩, 靳文舟, 巫威眺, 等. 考虑驾驶员需求的定位运输线路安排问题[J]. 广西大学学报 (自然科学版), 2020, 45(1): 229-238
ZHU Zixuan, JIN Wenzhou, WU Weitiao, et al. A location routing problem with driver needs[J]. Journal of Guangxi University (Natural Science Edition), 2020, 45(1): 229-238
[14] LI J, DU J, LI L. Optimization of vehicle routing problem with fatigue driving based on genetic algorithm [C/OL]. (2018-10-09). https://doi.org/10.1145/3264746.3264782.
[15] ZHANG S Z, LEE C K M, CHOY K L, et al. Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem[J]. Transportation Research Part D:Transport and Environment, 2014, 31: 85-99
[16] NG K K H, LEE C K M, ZHANG S Z, et al. A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion[J]. Computers & Industrial Engineering, 2017, 109: 151-168
[17] 张家善, 林晓群. 一种带自适应策略的改进蜂群算法及其在VRP的应用[J]. 数学的实践与认识, 2018, 48(23): 89-95
ZHANG Jiashan, LIN Xiaoqun. An improved artificial bee colony with self-adaptive strategies and application in the VRP[J]. Mathematics in Practice and Theory, 2018, 48(23): 89-95
[18] 陈皓, 张洁, 杨清萍, 等. 基于混合搜索的多种群人工蜂群算法[J]. 计算机应用, 2017, 37(10): 2773-2779, 2786
CHEN Hao, ZHANG Jie, YANG Qingping, et al. Multi-population artificial bee colony algorithm based on hybrid search[J]. Journal of Computer Applications, 2017, 37(10): 2773-2779, 2786
[19] GASTALDI M, ROSSI R, GECCHELE G. Effects of driver task-related fatigue on driving performance[J]. Procedia-Social and Behavioral Sciences, 2014, 111: 955-964
[20] JABER M Y, GIVI Z S, NEUMANN W P. Incorporating human fatigue and recovery into the learning-forgetting process[J]. Applied Mathematical Modelling, 2013, 37(12-13): 7287-7299

考虑驾驶员疲劳的车辆路径优化及算法研究

Vehicle Routing Optimization and Its Algorithm Considering Driver Fatigue

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	廖毅, 叶艳, 冷杰武. 车载可补货无人配送小车配送路径研究[J]. 工业工程, 2023, 26(1): 108-114.
[2]	杨静宗, 施春朝, 杨天晴, 吴丽玫. 基于LMD和ABC优化KELM的故障诊断方法[J]. 工业工程, 2022, 25(3): 124-131.
[3]	陈红梅, 张远航, 张春玲. 动态视角下城市通勤路径优化研究[J]. 工业工程, 2022, 25(1): 64-74.