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Routing for truck-drone collaborative distribution in epidemic areas: balancing risk and efficiency
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Changshi Liu1, Tao Liu2, Jingyi Ma3, Feng Wang1, **, Ming He4, Ke Tang1
China Safety Science Journal | 2026, 36(5) : 260 - 269
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China Safety Science Journal | 2026, 36(5): 260-269
• Public Safety and Emergency Management •
Routing for truck-drone collaborative distribution in epidemic areas: balancing risk and efficiency
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Changshi Liu1, Tao Liu2, Jingyi Ma3, Feng Wang1, **, Ming He4, Ke Tang1
Affiliations
  • 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
Published: 2026-05-28 doi: 10.16265/j.cnki.issn1003-3033.2026.05.2155
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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.

truck-drone collaborative delivery  /  emergency resource distribution  /  epidemic contagion risk  /  routes planning  /  distribution efficiency  /  improved multi-objective artificial bee colony algorithm (IMOABCA)
Changshi Liu, Tao Liu, Jingyi Ma, Feng Wang, Ming He, Ke Tang. Routing for truck-drone collaborative distribution in epidemic areas: balancing risk and efficiency[J]. China Safety Science Journal, 2026 , 36 (5) : 260 -269 . DOI: 10.16265/j.cnki.issn1003-3033.2026.05.2155
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Year 2026 volume 36 Issue 5
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.05.2155
  • Receive Date：2026-01-10
  • Online Date：2026-06-29
  • Published：2026-05-28
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  • Received：2026-01-10
  • Revised：2026-03-16
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Affiliations
    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
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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