Two-stage heuristic algorithm for vehicle-drone collaborative delivery and pickup based on medical supplies resource allocation
This paper aims to optimize medical material distribution in closed community logistics networks during sudden outbreaks with a focus on efficient waste collection and reduced consumable distribution time. First, considering the costs of UAV trajectory distribution, impact, threat, and other costs,...
Published in: | Journal of King Saud University - Computer and Information Sciences |
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Main Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Elsevier
2023
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Subjects: | |
Online Access: | https://doi.org/10.1016/j.jksuci.2023.101811 https://doaj.org/article/9be9f5f249724367a7d960e6906a6f8f |
Summary: | This paper aims to optimize medical material distribution in closed community logistics networks during sudden outbreaks with a focus on efficient waste collection and reduced consumable distribution time. First, considering the costs of UAV trajectory distribution, impact, threat, and other costs, a forward and reverse scheduling model with time windows for joint multi-distribution center material distribution and waste anti-epidemic materials collection vehicle-UAV is established. Meanwhile, a two-stage metaheuristic algorithm is proposed in this paper. In the first stage of the solution algorithm, we design the multi-strategy guided adaptive differential evolution (MSGA-DE) to plan the multi-UAV cooperative distribution situation in a 3D environment. In the second stage, an improved beluga whale optimization based on hybrid neighborhood search (HNS-IBWO) is combined to solve the vehicle-UAV scheduling and distribution problem. Furthermore, comparing with various cross-validation algorithms, it validates the superiority of MSGA-DE in solving UAV trajectory issues and the convergence speed and accuracy of HNS-IBWO for high-latitude complex optimizations. Finally, a simulation in a closed Shanghai community validates the proposed model. Results demonstrate its effectiveness, especially in terms of convergence, multi-objective search, and global search capabilities when compared to existing algorithms. This offers an efficient solution for vehicle-UAV scheduling in unforeseen epidemic-related closures. |
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