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A decomposition-based evolutionary algorithm for drone-assisted electric routing problem with drone stations
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AI
DOI：10.1080/00207543.2025.2609948.png)
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Abstract
En 中文
This paper focuses on an integrated routing problem incorporating electric vehicles (EVs), drones, and drone stations. Despite the considerable interest in employing drones in logistics as a tandem, previous studies indicated that adopting EV-drone tandems has a significant barrier due to the upfront investments to procure drones. To tackle this issue, this study explores the prospect of drone-as-a-service, employing drones owned by third-party providers to enhance logistics operations. The proposed system is formulated as a mixed-integer linear program with an objective function that minimises the system's makespan, which represents the completion time of the delivery mission. Considering the size and complexity of the model, we propose an evolutionary algorithm as a solver. The algorithm incorporates a customised solution representation, a specialised initialisation technique, and a local search technique to improve the algorithm's searching capability. Numerical experiments demonstrate the superior performance of our algorithm, showing gaps of 5.35-6.32%; compared to baseline algorithms. The results also indicate that incorporating DaaS providers can shorten the operations' makespan with up to 18.71%; gaps. Finally, the paper provides managerial insights into the proposed logistics system's benefits, challenges, and prospects.
Keywords:
Drone-as-a-service
dronesharing
electric vehicle
drone station
memetic algorithm
decomposition
Journal
IF:
7.3
Papers:
1.1W
Citations:
3.7W
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