Optimizing last-mile delivery services: a robust truck-drone cooperation model and hybrid metaheuristic algorithm

Seyed Mohammad Javad Mirzapour Al-E-Hashem, Taha-Hossein Hejazi, Ghazal Haghverdizadeh and Mohsen Shidpour
Additional contact information
Seyed Mohammad Javad Mirzapour Al-E-Hashem: AUT - Amirkabir University of Technology, Rennes SB - Rennes School of Business
Taha-Hossein Hejazi: AUT - Amirkabir University of Technology
Ghazal Haghverdizadeh: AUT - Amirkabir University of Technology
Mohsen Shidpour: AUT - Amirkabir University of Technology

Post-Print from HAL

Abstract: In response to the increasing demand for faster customer service and cost-effective solutions in competitive markets, many companies are exploring strategies and tools to streamline their services. One emerging approach involves the integration of drones with trucks, offering potential benefits such as reduced environmental impact and delivery time. This study focuses on the use of a single truck coordinating with multiple drones for postal package delivery. The drones are transported by the truck, and both vehicles are responsible for carrying out deliveries. To account for weather uncertainties, specifically wind direction and speed affecting drone travel time, a robust optimization model is developed to address the truck-drone routing problem. Additionally, a hybrid metaheuristic algorithm is proposed, combining Adaptive Large Neighborhood Search, Clarke and Wright Saving Algorithm, and Genetic Algorithm. The effectiveness of this algorithm is assessed through numerical experiments, including sensitivity analyses on key problem parameters. The findings demonstrate that the proposed model has practical applications in last-mile delivery services, while the algorithm provides near-optimal solutions within a reasonable timeframe (ALNS reaches the solutions 3500% faster than GAMS for small-sized problems in average). Also the results show that with the 100% increase in average distance between nodes in the network, the service time increases by more than 200%.

Keywords: Robust optimization; Uncertainty; Adaptive large neighborhood search algorithm; Hybrid truck-drone routing (search for similar items in EconPapers)
Date: 2024-07-26
References: Add references at CitEc
Citations:

Published in Annals of Operations Research, 2024, 364 (1), pp.795-825. ⟨10.1007/s10479-024-06164-5⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05741801

DOI: 10.1007/s10479-024-06164-5

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().