Vehicle routing problem with trucks and drones collaboration: a structured literature review

Xiaoteng Han; Jun Shen; Xiaoteng Han; Jun Shen

doi:10.3934/aci.2025020

Applied Computing and Intelligence

2025, Volume 5, Issue 2: 348-358. doi: 10.3934/aci.2025020

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Review

Vehicle routing problem with trucks and drones collaboration: a structured literature review

Xiaoteng Han ,
Jun Shen ^,

School of Computing and IT, University of Wollongong, NSW Australia

Academic Editor: Chih-Cheng Hung

Received: 04 December 2025 Revised: 24 December 2025 Accepted: 24 December 2025 Published: 29 December 2025

Coordinated truck-drone delivery has emerged as an important extension of the vehicle routing problem (VRP), offering new opportunities to improve logistics efficiency, accessibility, and sustainability. In this review, we synthesize the literature on truck-drone routing by examining three interrelated themes: Vehicle routing and synchronization models, stochastic and dynamic operational constraints, and payload-energy management. Early studies primarily formulate truck-drone delivery as variants of heterogeneous or two-echelon VRP using mixed-integer linear programming (MILP) to capture coupled routing decisions, drone endurance limits, and launch-retrieval feasibility, often supplemented by heuristic or metaheuristic strategies to address computational scalability. More recent research extends these formulations to stochastic and dynamic VRP settings by incorporating uncertain customer availability, time windows, vehicle delays, and weather disruptions, reflecting a shift toward adaptive routing under incomplete information. Parallel work on payload configuration and energy management investigates modular loading and battery-swapping mechanisms, demonstrating how load heterogeneity and energy constraints fundamentally reshape feasible routing structures. Extensions to multimodal VRP variants, including electric vehicle-drone systems and van-robot hybrids, further broaden applicability in dense and constrained urban environments. Despite these advances, most models remain grounded in static or weakly stochastic assumptions, with limited support for real-time updates or predictive decision-making. In this review, we identified the integration of data-driven prediction and learning-based optimization within dynamic VRP frameworks as a critical direction for advancing truck-drone delivery toward large-scale operational deployment.
- stochastic,
- vehicle routing problem (VRP),
- urban delivery,
- machine learning
Citation: Xiaoteng Han, Jun Shen. Vehicle routing problem with trucks and drones collaboration: a structured literature review[J]. Applied Computing and Intelligence, 2025, 5(2): 348-358. doi: 10.3934/aci.2025020

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Abstract

Coordinated truck-drone delivery has emerged as an important extension of the vehicle routing problem (VRP), offering new opportunities to improve logistics efficiency, accessibility, and sustainability. In this review, we synthesize the literature on truck-drone routing by examining three interrelated themes: Vehicle routing and synchronization models, stochastic and dynamic operational constraints, and payload-energy management. Early studies primarily formulate truck-drone delivery as variants of heterogeneous or two-echelon VRP using mixed-integer linear programming (MILP) to capture coupled routing decisions, drone endurance limits, and launch-retrieval feasibility, often supplemented by heuristic or metaheuristic strategies to address computational scalability. More recent research extends these formulations to stochastic and dynamic VRP settings by incorporating uncertain customer availability, time windows, vehicle delays, and weather disruptions, reflecting a shift toward adaptive routing under incomplete information. Parallel work on payload configuration and energy management investigates modular loading and battery-swapping mechanisms, demonstrating how load heterogeneity and energy constraints fundamentally reshape feasible routing structures. Extensions to multimodal VRP variants, including electric vehicle-drone systems and van-robot hybrids, further broaden applicability in dense and constrained urban environments. Despite these advances, most models remain grounded in static or weakly stochastic assumptions, with limited support for real-time updates or predictive decision-making. In this review, we identified the integration of data-driven prediction and learning-based optimization within dynamic VRP frameworks as a critical direction for advancing truck-drone delivery toward large-scale operational deployment.

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