疫区卡车-无人机协同配送路径规划:风险与效率兼顾

doi:10.16265/j.cnki.issn1003-3033.2026.05.2155

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (5): 260-269.doi: 10.16265/j.cnki.issn1003-3033.2026.05.2155

疫区卡车-无人机协同配送路径规划:风险与效率兼顾

刘长石¹(), 刘涛², 马敬仪³, 王凤¹^,^**(), 何鸣⁴, 汤柯¹

¹ 湖南工商大学工商管理学院, 湖南长沙 410205
² 中南大学商学院, 湖南长沙 410083
³ 湖南工商大学前沿交叉学院, 湖南长沙 410205
⁴ 湖南大学工商管理学院, 湖南长沙 410082

收稿日期:2026-01-10 修回日期:2026-03-16 出版日期:2026-05-28
通信作者:
^** 王凤(1977—),女,湖南攸县人,博士,副教授,主要从事物流与供应链方面的研究。E-mail:wf@hutb.edu.cn。
作者简介:
刘长石 (1975—),男,湖南邵阳人,博士,教授,博士生导师,主要从事物流管理、数字经济方面的研究。E-mail:liuchangshi964@126.com。
基金资助:
国家社会科学基金资助(23BGL011); 国家社会科学基金资助(21BJY191); 湖南省教育厅科学研究重点项目(23A0463); 湖南省社科基金资助(23JD037); 长沙市自然科学基金资助(kq2502045)

Routing for truck-drone collaborative distribution in epidemic areas: balancing risk and efficiency

Liu Changshi¹(), Liu Tao², Ma Jingyi³, Wang Feng¹^,^**(), He Ming⁴, Tang Ke¹

¹ School of Management, Hunan University of Technology and Business, Changsha Hunan 410205, China
² School of Business, Central South University, Changsha Hunan 410083, China
³ School of Frontier Interdisciplinary, Hunan University of Technology and Business, Changsha Hunan 410205, China
⁴ School of Business, Hunan University, Changsha Hunan 410082, China

Received:2026-01-10 Revised:2026-03-16 Published:2026-05-28

摘要/Abstract

摘要：

为有效降低疫区应急物流“最后一公里”的疫情传染风险,首先,设计卡车-无人机协同配送模式,基于传染病的传播动力学模型构建“基本再生数”函数量化各需求点的疫情感染人数;然后,以最小化所有需求点的疫情感染人数、最小化总配送时间为目标,构建卡车-无人机协同配送应急物资的路径规划模型,并结合模型多目标、非线性的特性,设计一种改进的多目标人工蜂群算法(IMOABCA)求解;最后,采用多类型算例开展试验。结果表明:IMOABCA能综合考虑疫区疫情、需求点分布与人口数量等情况,科学规划卡车-无人机协同配送应急物资的路径方案,不但能有效降低疫情传染风险,并能保障应急物资配送的时效性。与基础多目标人工蜂群算法(MOABCA)、非支配排序遗传算法-II(NSGA-II)求得的配送路径方案相比,IMOABCA求得规划方案的总感染人数分别减少922和746人,总配送时间分别节省3.71%和1.41%,任务完成时间分别缩短14.06%和3.6%。

关键词: 卡车-无人机协同配送, 应急物资配送, 疫情传染风险, 路径规划, 配送效率, 改进的多目标人工蜂群算法(IMOABCA)

Abstract:

To effectively reduce the contagion risks in the "last-mile" of emergency logistics in epidemic-stricken areas, a truck-drone collaborative delivery mode was first designed. A "basic reproduction number" function was constructed based on epidemic transmission dynamics to quantify the number of infections at various demand points. Then, a routing optimization model for truck-drone collaborative emergency supply delivery was established, aiming to minimize both the total number of infections and the total delivery time. In view of the multi-objective and non-linear characteristics of the model, the IMOABCA was developed. Finally, experiments were carried out through multiple types of instances. The results show that the IMOABCA could scientifically optimize delivery routes by integrating epidemic data, demand point distribution, and population size. Compared with the basic multi-objective artificial bee colony algorithm (MOABC)and Non-dominated Sorting Genetic Algorithm-II(NSGA-II), the total number of infections is reduced by 922 and 746, respectively. Additionally, the total delivery time is saved by 3.71% and 1.41%, and the task completion time can be shortened by 14.06% and 3.6%, respectively.

Key words: truck-drone collaborative delivery, emergency resource distribution, epidemic contagion risk, routes planning, distribution efficiency, improved multi-objective artificial bee colony algorithm (IMOABCA)

中图分类号:

刘长石, 刘涛, 马敬仪, 王凤, 何鸣, 汤柯. 疫区卡车-无人机协同配送路径规划:风险与效率兼顾[J]. 中国安全科学学报, 2026, 36(5): 260-269.

Liu Changshi, Liu Tao, Ma Jingyi, Wang Feng, He Ming, Tang Ke. Routing for truck-drone collaborative distribution in epidemic areas: balancing risk and efficiency[J]. China Safety Science Journal, 2026, 36(5): 260-269.

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算例	OT/min	TCT/min	VDT/min	DDT/min	IC/人	AP/人	IP/%	RT/s
C102	2 021.89	596.37	600.43	1 421.46	2 664	90 500	2.9	42.76
R108	1 860.96	773.58	614.18	1 246.79	6 363	72 900	8.7	42.36
RC105	2 150.80	806.96	804.68	1 346.12	8 480	86 200	9.8	41.93
27T1	1 798.70	925.75	843.21	955.49	5 013	50 000	10.0	42.22
28T1	1 920.28	1 100.99	847.37	1 072.91	5 628	50 000	11.2	42.96
44T1	1 458.87	826.35	686.01	772.86	4 840	50 000	9.6	40.20
60T1	1 687.37	987.80	902.18	785.20	4 921	50 000	9.8	41.45

算法	OT/min	TCT/min	IC/人	HV	RT/s
IMOABCA	2 020.11	562.07	3239	0.51	44.65
MOABC	2 098.59	654.88	4161	0.34	38.28
NSGA-II	2 049.26	583.38	3985	0.09	36.44

方案	OT/min	TCT/min	IC/人
1	2 049.01	738.75	8 425
2	2 015.18	682.04	8 660
3	2 100.41	783.19	8 356

载货量/kg	OT/min	TCT/min	IC/人	TDT/min
15	2 125.00	855.44	7 422	695
20	1 900.10	801.70	7 270	627
25	1 723.61	729.97	7 133	577
30	1 634.13	646.92	7 096	537

疫区卡车-无人机协同配送路径规划:风险与效率兼顾

Routing for truck-drone collaborative distribution in epidemic areas: balancing risk and efficiency

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