A literature review of drone-truck routing problems: challenges and future research directions

Open Access

Issue		RAIRO-Oper. Res. Volume 59, Number 5, September-October 2025


Page(s)		3169 - 3205
DOI		https://doi.org/10.1051/ro/2025119
Published online		24 October 2025

M. Hamid, M.M. Nasiri and M. Rabbani, A mixed closed-open multi-depot routing and scheduling problem for homemade meal delivery incorporating drone and crowd-sourced fleet: a self-adaptive hyper-heuristic approach. Eng. App. Artif. Intell. 120 (2023) 105876. [Google Scholar]
A. Arishi, K. Krishnan and M. Arishi, Machine learning approach for truck-drones based last-mile delivery in the era of industry 4.0. Eng. App. Artif. Intell. 116 (2022) 105439. [Google Scholar]
R.I. Mukhamediev, K. Yakunin, M. Aubakirov, I. Assanov, Y. Kuchin, A. Symagulov, V. Levashenko, E. Zaitseva, D. Sokolov and Y. Amirgaliyev, Coverage path planning optimization of heterogeneous UAVs group for precision agriculture. IEEE Access 11 (2023) 5789–5803. [Google Scholar]
Y. Xu, X. Xue, Z. Sun, W. Gu, L. Cui, Y. Jin and Y. Lan, Joint path planning and scheduling for vehicle-assisted multiple unmanned aerial systems plant protection operation. Comput. Electron. Agric. 200 (2022) 107221. [Google Scholar]
Y. Xu, Z. Sun, X. Xue, W. Gu and B. Peng, A hybrid algorithm based on MOSFLA and GA for multi-UAVs plant protection task assignment and sequencing optimization. Appl. Soft Comput. 96 (2020) 106623. [Google Scholar]
U.M.R. Mogili and B.B.V.L. Deepak, Review on application of drone systems in precision agriculture. Proc. Comput. Sci. 133 (2018) 502–509. [Google Scholar]
M. Joerss, F. Neuhaus and J. Schröder, How customer demands are reshaping last-mile delivery. McKinsey Q. 17 (2016) 1–5. [Google Scholar]
Y. Xia, W. Zeng, C. Zhang and H. Yang, A branch-and-price-and-cut algorithm for the vehicle routing problem with load-dependent drones. Transp. Res. Part B: Methodol. 171 (2023) 80–110. [Google Scholar]
B. Skorup and C. Haaland, How drones can help fight the coronavirus, in Mercatus Center Research Paper Series, Special Edition Policy Brief (2020). [Google Scholar]
C.C. Murray and A.G. Chu, The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transp. Res. Part C: Emerg. Technol. 54 (2015) 86–109. [Google Scholar]
A. Otto, N. Agatz, J. Campbell, B. Golden and E. Pesch, Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: a survey. Networks 72 (2018) 411–458. [CrossRef] [MathSciNet] [Google Scholar]
I. Khoufi, A. Laouiti and C. Adjih, A survey of recent extended variants of the traveling salesman and vehicle routing problems for unmanned aerial vehicles. Drones 3 (2019) 66. [Google Scholar]
S.H. Chung, B. Sah and J. Lee, Optimization for drone and drone-truck combined operations: a review of the state of the art and future directions. Comput. Oper. Res. 123 (2020) 105004. [Google Scholar]
G. Macrina, L.D.P. Pugliese, F. Guerriero and G. Laporte, Drone-aided routing: a literature review. Transp. Res. Part C: Emerg. Technol. 120 (2020) 102762. [Google Scholar]
D. Rojas Viloria, E.L. Solano-Charris, A. Mu˜noz-Villamizar and J.R. Montoya-Torres, Unmanned aerial vehicles/drones in vehicle routing problems: a literature review. Int. Trans. Oper. Res. 28 (2021) 1626–1657. [Google Scholar]
M. Moshref-Javadi and M. Winkenbach, Applications and research avenues for drone-based models in logistics: a classification and review. Expert Syst. App. 177 (2021) 114854. [Google Scholar]
T. Benarbia and K. Kyamakya, A literature review of drone-based package delivery logistics systems and their implementation feasibility. Sustainability 14 (2021) 360. [Google Scholar]
Y.-J. Liang and Z.-X. Luo, A survey of truck–drone routing problem: literature review and research prospects. J. Oper. Res. Soc. Chin. 10 (2022) 343–377. [Google Scholar]
R. Zhang, L. Dou, B. Xin, C. Chen, F. Deng and J. Chen, A review on the truck and drone cooperative delivery problem. Unmanned Syst. 12 (2024) 823–847. [Google Scholar]
N. Agatz, P. Bouman and M. Schmidt, Optimization approaches for the traveling salesman problem with drone. Transp. Sci. 52 (2018) 965–981. [CrossRef] [Google Scholar]
P. Bouman, N. Agatz and M. Schmidt, Dynamic programming approaches for the traveling salesman problem with drone. Networks 72 (2018) 528–542. [CrossRef] [MathSciNet] [Google Scholar]
Q.M. Ha, Y. Deville, Q.D. Pham and M.H. Hà, On the min-cost traveling salesman problem with drone. Transp. Res. Part C: Emerg. Technol. 86 (2018) 597–621. [Google Scholar]
Q.M. Ha, Y. Deville, Q.D. Pham and M.H. Hà, A hybrid genetic algorithm for the traveling salesman problem with drone. J. Heuristics 26 (2020) 219–247. [Google Scholar]
J. Liu, Z. Guan and X. Xie, Truck and drone in tandem route scheduling under sparse demand distribution, in: 2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS). IEEE (2018) 1–6. [Google Scholar]
M. Marinelli, L. Caggiani, M. Ottomanelli and M. Dell’Orco, En route truck–drone parcel delivery for optimal vehicle routing strategies. IET Intell. Transp. Syst. 12 (2018) 253–261. [Google Scholar]
S. Poikonen, B. Golden and E.A. Wasil, A branch-and-bound approach to the traveling salesman problem with a drone. INFORMS J. Comput. 31 (2019) 335–346. [Google Scholar]
M.A. Nguyen, K. Sano and V.T. Tran, A Monte Carlo tree search for traveling salesman problem with drone. Asian Transp. Stud. 6 (2020) 100028. [Google Scholar]
P.L. Gonzalez-R, D. Canca, J.L. Andrade-Pineda, M. Calle and J.M. Leon-Blanco, Truck-drone team logistics: a heuristic approach to multi-drop route planning. Transp. Res. Part C: Emerg. Technol. 114 (2020) 657–680. [Google Scholar]
R. Rich, Inverting the truck-drone network problem to find best case configuration. Adv. Oper. Res. 2020 (2020) 4053983. [Google Scholar]
S.A. Vásquez, G. Angulo and M.A. Klapp, An exact solution method for the TSP with drone based on decomposition. Comput. Oper. Res. 127 (2021) 105127. [CrossRef] [Google Scholar]
R. Roberti and M. Ruthmair, Exact methods for the traveling salesman problem with drone. Transp. Sci. 55 (2021) 315–335. [Google Scholar]
A. Ponza, Optimization of drone-assisted parcel delivery. Master Thesis, Universita Degli Studi Di Padova (2016). [Google Scholar]
S. Mourelo Ferrandez, T. Harbison, T. Webwer, R. Sturges and R. Rich, Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm. J. Ind. Eng. Manage. 9 (2016) 374–388. [Google Scholar]
J.C. De Freitas and P.H.V. Penna, A variable neighborhood search for flying sidekick traveling salesman problem. Int. Trans. Oper. Res. 27 (2020) 267–290. [Google Scholar]
A.M. Ham, Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming. Transp. Res. Part C: Emerg. Technol. 91 (2018) 1–14. [Google Scholar]
R.G. Mbiadou Saleu, L. Deroussi, D. Feillet, N. Grangeon and A. Quilliot, An iterative two-step heuristic for the parallel drone scheduling traveling salesman problem. Networks 72 (2018) 459–474. [Google Scholar]
S. Kim and I. Moon, Traveling salesman problem with a drone station. IEEE Trans. Syst. Man Cybern.: Syst. 49 (2018) 42–52. [Google Scholar]
M. Dell’Amico, R. Montemanni and S. Novellani, Matheuristic algorithms for the parallel drone scheduling traveling salesman problem. Ann. Oper. Res. 289 (2020) 211–226. [Google Scholar]
Z. Wang and J.-B. Sheu, Vehicle routing problem with drones. Transp. Res. Part B: Methodol. 122 (2019) 350–364. [Google Scholar]
P.L. Gonzalez-R, D. Sanchez-Wells and J.L. Andrade-Pineda, A bi-criteria approach to the truck-multidrone routing problem. Expert Syst. Appl. 243 (2024) 122809. [Google Scholar]
M. Boccia, A. Mancuso, A. Masone, T. Murino and C. Sterle, New features for customer classification in the flying sidekick traveling salesman problem. Expert Syst. Appl. 247 (2024) 123106. [Google Scholar]
S. Meng, Y. Chen and D. Li, The multi-visit drone-assisted pickup and delivery problem with time windows. Eur. J. Oper. Res. 314 (2024) 685–702. [Google Scholar]
Q. Luo, G. Wu, A. Trivedi, F. Hong, L. Wang and D. Srinivasan, Multi-objective optimization algorithm with adaptive resource allocation for truck-drone collaborative delivery and pick-up services. IEEE Trans. Intell. Transp. Syst. 24 (2023) 9642–9657. [Google Scholar]
J. Huang, Y. Luo, Q. Quan, B. Wang, X. Xue and Y. Zhang, An autonomous task assignment and decision-making method for coverage path planning of multiple pesticide spraying UAVs. Comput. Electron. Agric. 212 (2023) 108128. [Google Scholar]
S. Fang, Y. Ru, C. Hu, F. Yang, J. Xue and J. Zhou, Planning the temporary takeoff/landing site’s location for a pesticide spraying helicopter based on an intelligent fusion algorithm. Comput. Electron. Agric. 209 (2023) 107826. [Google Scholar]
A. Bogyrbayeva, T. Yoon, H. Ko, S. Lim, H. Yun and C. Kwon, A deep reinforcement learning approach for solving the traveling salesman problem with drone. Transp. Res. Part C: Emerg. Technol. 148 (2023) 103981. [Google Scholar]
Z. Bi, X. Guo, J. Wang, S. Qin and G. Liu, Deep reinforcement learning for truck-drone delivery problem. Drones 7 (2023) 445. [Google Scholar]
M.A. Boschetti and S. Novellani, Last-mile delivery with drone and lockers. Networks 83 (2024) 213–235. [Google Scholar]
H. Li and F. Wang, Branch-price-and-cut for the truck–drone routing problem with time windows. Nav. Res. Logistics (NRL) 70 (2023) 184–204. [Google Scholar]
T. Thomas, S. Srinivas and C. Rajendran, Collaborative truck multi-drone delivery system considering drone scheduling and en route operations. Ann. Oper. Res. 339 (2024) 693–739. [Google Scholar]
S. Meng, X. Guo, D. Li and G. Liu, The multi-visit drone routing problem for pickup and delivery services. Transp. Res. Part E: Logistics Transp. Rev. 169 (2023) 102990. [Google Scholar]
F. Tamke and U. Buscher, The vehicle routing problem with drones and drone speed selection. Comput. Oper. Res. 152 (2023) 106112. [Google Scholar]
Y. Li, Y. Wu, X. Xue, X. Liu, Y. Xu and X. Liu, Efficiency-first spraying mission arrangement optimization with multiple UAVs in heterogeneous farmland with varying pesticide requirements. Inf. Process. Agric. 11 (2024) 237–248. [Google Scholar]
J. Zhang and Y. Li, Collaborative vehicle-drone distribution network optimization for perishable products in the epidemic situation. Comput. Oper. Res. 149 (2023) 106039. [Google Scholar]
J. Chen, R. Zhang, H. Zhao, J. Li and J. He, Path planning of multiple unmanned aerial vehicles covering multiple regions based on minimum consumption ratio. Aerospace 10 (2023) 93. [Google Scholar]
Z. Luo, R. Gu, M. Poon, Z. Liu and A. Lim, A last-mile drone-assisted one-to-one pickup and delivery problem with multi-visit drone trips. Comput. Oper. Res. 148 (2022) 106015. [Google Scholar]
R.J. Kuo, S. H. Lu, P.Y. Lai and S.T.W. Mara, Vehicle routing problem with drones considering time windows. Expert Syst. Appl. 191 (2022) 116264. [Google Scholar]
D. Lei, Z. Cui and M. Li, A dynamical artificial bee colony for vehicle routing problem with drones. Eng. App. Artif. Intell. 107 (2022) 104510. [Google Scholar]
Y. Li, Y. Xu, X. Xue, X. Liu and X. Liu, Optimal spraying task assignment problem in crop protection with multi-UAV systems and its order irrelevant enumeration solution. Biosyst. Eng. 214 (2022) 177–192. [Google Scholar]
T. Bányai, Impact of the integration of first-mile and last-mile drone-based operations from trucks on energy efficiency and the environment. Drones 6 (2022) 249. [Google Scholar]
R. Gu, M. Poon, Z. Luo, Y. Liu and Z. Liu, A hierarchical solution evaluation method and a hybrid algorithm for the vehicle routing problem with drones and multiple visits. Transp. Res. Part C: Emerg. Technol. 141 (2022) 103733. [Google Scholar]
X. Wen and G. Wu, Heterogeneous multi-drone routing problem for parcel delivery. Transp. Res. Part C: Emerg. Technol. 141 (2022) 103763. [Google Scholar]
M.A. Nguyen, G.T.H. Dang, M.H. Hà and M.T. Pham, The min-cost parallel drone scheduling vehicle routing problem. Eur. J. Oper. Res. 299 (2022) 910–930. [Google Scholar]
Y. Wang, Z. Wang, X. Hu, G. Xue and X. Guan, Truck–drone hybrid routing problem with time-dependent road travel time. Transp. Res. Part C: Emerg. Technol. 144 (2022) 103901. [Google Scholar]
M.A. Masmoudi, S. Mancini, R. Baldacci and Y.H. Kuo, Vehicle routing problems with drones equipped with multi-package payload compartments. Transp. Res. Part E: Logistics Transp. Rev. 164 (2022) 102757. [CrossRef] [Google Scholar]
L. Zhen, J. Gao, Z. Tan, S. Wang and R. Baldacci, Branch-price-and-cut for trucks and drones cooperative delivery. IISE Trans. 55 (2023) 271–287. [Google Scholar]
J.C. Pina-Pardo, D.F. Silva and A.E. Smith, The traveling salesman problem with release dates and drone resupply. Comput. Oper. Res. 129 (2021) 105170. [Google Scholar]
F. Tamke and U. Buscher, A branch-and-cut algorithm for the vehicle routing problem with drones. Transp. Res. Part B: Methodol. 144 (2021) 174–203. [Google Scholar]
M. Boccia, A. Masone, A. Sforza and C. Sterle, An exact approach for a variant of the FS-TSP. Transp. Res. Proc. 52 (2021) 51–58. [Google Scholar]
S. Cavani, M. Iori and R. Roberti, Exact methods for the traveling salesman problem with multiple drones. Transp. Res. Part C: Emerg. Technol. 130 (2021) 103280. [Google Scholar]
M. Boccia, A. Masone, A. Sforza and C. Sterle, A column-and-row generation approach for the flying sidekick travelling salesman problem. Transp. Res. Part C: Emerg. Technol. 124 (2021) 102913. [Google Scholar]
J. Euchi and A. Sadok, Hybrid genetic-sweep algorithm to solve the vehicle routing problem with drones. Phys. Commun. 44 (2021) 101236. [Google Scholar]
Z. Luo, M. Poon, Z. Zhang, Z. Liu and A. Lim, The multi-visit traveling salesman problem with multi-drones. Transp. Res. Part C: Emerg. Technol. 128 (2021) 103172. [Google Scholar]
H. Baik and J. Valenzuela, An optimization drone routing model for inspecting wind farms. Soft Comput. 25 (2021) 2483–2498. [Google Scholar]
M. Dell’Amico, R. Montemanni and S. Novellani, Modeling the flying sidekick traveling salesman problem with multiple drones. Networks 78 (2021) 303–327. [Google Scholar]
Y. Liu, Z. Liu, J. Shi, G. Wu and W. Pedrycz, Two-echelon routing problem for parcel delivery by cooperated truck and drone. IEEE Trans. Syst. Man Cybern.: Syst. 51 (2020) 7450–7465. [Google Scholar]
S. Poikonen and B. Golden, The mothership and drone routing problem. INFORMS J. Comput. 32 (2020) 249–262. [Google Scholar]
P. Deng, G. Amirjamshidi and M. Roorda, A vehicle routing problem with movement synchronization of drones, sidewalk robots, or foot-walkers. Transp. Res. Proc. 46 (2020) 29–36. [Google Scholar]
P. Kitjacharoenchai, B.C. Min and S. Lee, Two echelon vehicle routing problem with drones in last mile delivery. Int. J. Prod. Econ. 225 (2020) 107598. [Google Scholar]
I. Dayarian, M. Savelsbergh and J.P. Clarke, Same-day delivery with drone resupply. Transp. Sci. 54 (2020) 229–249. [Google Scholar]
M. Salama and S. Srinivas, Joint optimization of customer location clustering and drone-based routing for last-mile deliveries. Transp. Res. Part C: Emerg. Technol. 114 (2020) 620–642. [Google Scholar]
C.C. Murray and R. Raj, The multiple flying sidekicks traveling salesman problem: parcel delivery with multiple drones. Transp. Res. Part C: Emerg. Technol. 110 (2020) 368–398. [Google Scholar]
D.N. Das, R. Sewani, J. Wang and M.K. Tiwari, Synchronized truck and drone routing in package delivery logistics. IEEE Trans. Intell. Transp. Syst. 22 (2020) 5772–5782. [Google Scholar]
H. Li, H. Wang, J. Chen and M. Bai, Two-echelon vehicle routing problem with time windows and mobile satellites. Transp. Res. Part B: Methodol. 138 (2020) 179–201. [Google Scholar]
H. Huang, A.V. Savkin and C. Huang, Scheduling of a parcel delivery system consisting of an aerial drone interacting with public transportation vehicles. Sensors 20 (2020) 2045. [Google Scholar]
S. Poikonen and B. Golden, Multi-visit drone routing problem. Comput. Oper. Res. 113 (2020) 104802. [CrossRef] [MathSciNet] [Google Scholar]
G.C. Cri¸san and E. Nechita, On a cooperative truck-and-drone delivery system. Proc. Comput. Sci. 159 (2019) 38–47. [Google Scholar]
P. Kitjacharoenchai, M. Ventresca, M. Moshref-Javadi, S. Lee, J.M. Tanchoco and P.A. Brunese, Multiple traveling salesman problem with drones: mathematical model and heuristic approach. Comput. Ind. Eng. 129 (2019) 14–30. [Google Scholar]
D. Schermer, M. Moeini and O. Wendt, A matheuristic for the vehicle routing problem with drones and its variants. Transp. Res. Part C: Emerg. Technol. 106 (2019) 166–204. [Google Scholar]
H.Y. Jeong, B.D. Song and S. Lee, Truck-drone hybrid delivery routing: payload-energy dependency and no-fly zones. Int. J. Prod. Econ. 214 (2019) 220–233. [CrossRef] [Google Scholar]
D. Schermer, M. Moeini and O. Wendt, A hybrid VNS/Tabu search algorithm for solving the vehicle routing problem with drones and en route operations. Comput. Oper. Res. 109 (2019) 134–158. [Google Scholar]
D. Sacramento, D. Pisinger and S. Ropke, An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones. Transp. Res. Part C: Emerg. Technol. 102 (2019) 289–315. [Google Scholar]
W.C. Chiang, Y. Li, J. Shang and T.L. Urban, Impact of drone delivery on sustainability and cost: realizing the UAV potential through vehicle routing optimization. Appl. Energy 242 (2019) 1164–1175. [Google Scholar]
A. Karak and K. Abdelghany, The hybrid vehicle-drone routing problem for pick-up and delivery services. Transp. Res. Part C: Emerg. Technol. 102 (2019) 427–449. [Google Scholar]
P. Kitjacharoenchai and S. Lee, Vehicle routing problem with drones for last mile delivery. Proc. Manuf. 39 (2019) 314–324. [Google Scholar]
Y. Liu, Z. Luo, Z. Liu, J. Shi and G. Cheng, Cooperative routing problem for ground vehicle and unmanned aerial vehicle: the application on intelligence, surveillance, and reconnaissance missions. IEEE Access 7 (2019) 63504–63518. [Google Scholar]
K. Peng, J. Du, F. Lu, Q. Sun, Y. Dong, P. Zhou and M. Hu, A hybrid genetic algorithm on routing and scheduling for vehicle-assisted multi-drone parcel delivery. IEEE Access 7 (2019) 49191–49200. [Google Scholar]
D. Wang, P. Hu, J. Du, P. Zhou, T. Deng and M. Hu, Routing and scheduling for hybrid truck-drone collaborative parcel delivery with independent and truck-carried drones. IEEE Int. Things J. 6 (2019) 10483–10495. [Google Scholar]
D. Schermer, M. Moeini and O. Wendt, The traveling salesman drone station location problem, in World Congress on Global Optimization. Springer, Cham (2019). [Google Scholar]
J.G. Carlsson and S. Song, Coordinated logistics with a truck and a drone. Manage. Sci. 64 (2018) 4052–4069. [Google Scholar]
Y.S. Chang and H.J. Lee, Optimal delivery routing with wider drone-delivery areas along a shorter truck-route. Expert Syst. Appl. 104 (2018) 307–317. [Google Scholar]
E.E. Yurek and H.C. Ozmutlu, A decomposition-based iterative optimization algorithm for traveling salesman problem with drone. Transp. Res. Part C: Emerg. Technol. 91 (2018) 249–262. [Google Scholar]
K. Peng, W. Liu, Q. Sun, X. Ma, M. Hu, D. Wang and J. Liu, Wide-area vehicle-drone cooperative sensing: opportunities and approaches. IEEE Access 7 (2018) 1818–1828. [Google Scholar]
D. Schermer, M. Moeini and O. Wendt, Algorithms for solving the vehicle routing problem with drones, in Asian Conference on Intelligent Information and Database Systems. Springer, Cham (2018) 352–361. [Google Scholar]
M.W. Ulmer and B.W. Thomas, Same-day delivery with heterogeneous fleets of drones and vehicles. Networks 72 (2018) 475–505. [Google Scholar]
M. Hu, W. Liu, K. Peng, X. Ma, W. Cheng, J. Liu and B. Li, Joint routing and scheduling for vehicle-assisted multidrone surveillance. IEEE Int. Things J. 6 (2018) 1781–1790. [Google Scholar]
N. Boysen, D. Briskorn, S. Fedtke and S. Schwerdfeger, Drone delivery from trucks: drone scheduling for given truck routes. Networks 72 (2018) 506–527. [Google Scholar]
B.S. Faiçal, H. Freitas, P.H. Gomes, L.Y. Mano, G. Pessin, A.C. de Carvalho and J. Ueyama, An adaptive approach for UAV-based pesticide spraying in dynamic environments. Comput. Electron. Agric. 138 (2017) 210–223. [Google Scholar]
Z. Luo, Z. Liu and J. Shi, A two-echelon cooperated routing problem for a ground vehicle and its carried unmanned aerial vehicle. Sensors 17 (2017) 1144. [Google Scholar]
S. Poikonen, X. Wang and B. Golden, The vehicle routing problem with drones: extended models and connections. Networks 70 (2017) 34–43. [Google Scholar]
Y. Guo, F. Zhang, S. Chang, Z. Li and Z. Li, Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows. Comput. Electron. Agric. 224 (2024) 109121. [Google Scholar]
M. Ariza-Sentís, S. Vélez, H. Baja, R.G. Valenti and J. Valente, An aerial framework for Multi-View grape bunch detection and route Optimization using ACO. Comput. Electron. Agric. 221 (2024) 108972. [Google Scholar]
J. Jiang, Y. Dai, F. Yang and Z. Ma, A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services. Eur. J. Oper. Res. 312 (2024) 125–137. [Google Scholar]
Y. Tang, K. Huang, Z. Tan, M. Fang and H. Huang, Multi-subswarm cooperative particle swarm optimization algorithm and its application. Inf. Sci. 677 (2024) 120887. [Google Scholar]
T.Y. Chen, Z.H. Miao, W.M. Li and Q.K. Pan, A learning-based memetic algorithm for a cooperative task allocation problem of multiple unmanned aerial vehicles in smart agriculture. Swarm Evol. Comput. 91 (2024) 101694. [Google Scholar]
M. Plessen, Path planning for spot spraying with UAVs combining tsp and area coverages. Smart Agric. Technol. 11 (2025) 100965. [Google Scholar]
Z. Jiang, K. Meng and C. Chen, Coverage path planning based on recursive polygonal decomposition for multiple regions, in 2024 43rd Chinese Control Conference (CCC). IEEE (2024) 2076–2081. [Google Scholar]
J. Huang, B. Du, Y. Zhang, Q. Quan, B. Wang and L. Mu, A pesticide spraying mission allocation and path planning with multicopters. IEEE Trans. Aerosp. Electron. Syst. 60 (2024) 2277–2291. [Google Scholar]
J. Liu, Y. Lin, X. Zhang, J. Yin, X. Zhang, Y. Feng and Q. Qian, Agricultural UAV path planning based on a differentiated creative search algorithm with multi-strategy improvement. Machines 12 (2024) 591. [Google Scholar]
Y. Long, G. Xu, J. Zhao, B. Xie and M. Fang, Dynamic truck–UAV collaboration and integrated route planning for resilient urban emergency response. IEEE Trans. Eng. Manage. 71 (2024) 9826–9838. [Google Scholar]
T.I. Faiz, C. Vogiatzis, J. Liu and M. Noor-E-Alam, A robust optimization framework for two-echelon vehicle and UAV routing for post-disaster humanitarian logistics operations. Networks 84 (2024) 200–219. [Google Scholar]
T. Calamoneri, F. Corò and S. Mancini, Management of a post-disaster emergency scenario through unmanned aerial vehicles: multi-depot multi-trip vehicle routing with total completion time minimization. Expert Syst. Appl. 251 (2024) 123766. [Google Scholar]
J. Liu, Z. Guan and X. Xie, Truck and drone in tandem route scheduling under sparse demand distribution, in 2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS). IEEE (2018) 1–6. [Google Scholar]
S. Tian, X. Wen, B. Wei and G. Wu, Cooperatively routing a truck and multiple drones for target surveillance. Sensors 22 (2024) 2909. [Google Scholar]
Y. Luo, X. Deng, W. Zhang, Y. Ke, S. Wan and Y. Qian, Collaborative intelligent delivery with one truck and multiple heterogeneous drones in covid-19 pandemic environment. IEEE Trans. Intell. Transp. Syst. 25 (2024) 7907–7920. [Google Scholar]
M. Lin, Y. Chen, R. Han and Y. Chen, Discrete optimization on truck-drone collaborative transportation system for delivering medical resources. Discrete Dyn. Nat. Soc. 2022 (2022) 1811288. [Google Scholar]
P. Bowes, Pitney bowes parcel shipping index. Available online: https://www.pitneybowes.com/us/shipping-index.html (2022). Accessed on 13 December 2022. [Google Scholar]
M. Dektas, Workhorse group receives patent for horsefly delivery truck-launched drone package delivery system. Available online: https://www.sonnenseite.com/en/future/workhorse-group-receives-patent-for-horsefly-delivery-truck-launched-drone-package-delivery-system.html (2018). Accessed on 02 February 2023. [Google Scholar]
G.Q. Li, X.G. Zhou, J. Yin and Q.Y. Xiao, An UAV scheduling and planning method for post-disaster survey. Int. Arch. Photogrammetry Remote Sens. Spatial Inf. Sci. 40 (2014) 169–172. [Google Scholar]
X. Weng, W. She, H. Fan, J. Zhang and L. Yun, Multi-depot vehicle routing problem with drones in emergency logistics. Cluster Comput. 28 (2025) 1–27. [Google Scholar]
W. Peng, D. Wang, Y. Yin and T.C.E. Cheng, Multi-agent deep reinforcement learning-based truck-drone collaborative routing with dynamic emergency response. Transp. Res. Part E: Logistics Transp. Rev. 195 (2025) 103974. [Google Scholar]
G. Xu and Q. Lyu, Vehicle routing problem for collaborative multidepot petrol replenishment under emergency conditions. J. Adv. Transp. 2021 (2021) 5531500. [Google Scholar]
S. Wang, C. Zheng and S. Wandelt, Policy challenges for coordinated delivery of trucks and drones. J. Air Transp. Res. Soc. 2 (2024) 100001. [Google Scholar]
C. Kaplan, Everyday militarisms: drones and the blurring of the civilian–military divide during COVID-19, in Drone Aesthetics (2024) 98. [Google Scholar]
J. Euchi, Do drones have a realistic place in a pandemic fight for delivering medical supplies in healthcare systems problems? Chin. J. Aeronaut. 34 (2021) 182–190. [Google Scholar]
R. Sham, C.S. Siau, S. Tan, D.C. Kiu, H. Sabhi, H.Z. Thew and M.H.M. Ramli, Drone usage for medicine and vaccine delivery during the covid-19 pandemic: attitude of health care workers in rural medical centres. Drones 6 (2022) 109. [Google Scholar]
S.H. Lu, M.F. Benaglia, A.T. Nguyen, E.R. Rivera and J.W. Cheng, Vehicle routing problem with drones as an aid for epidemic relief. Int. J. Shipping Transp. Logistics 18 (2024) 249–280. [Google Scholar]
P. Qi, L. Zhang, Z. Wang, H. Han, J. Müller, T. Li and X. He, Effect of operational parameters of unmanned aerial vehicle (UAV) on droplet deposition in trellised pear orchard. Drones 7 (2023) 57. [Google Scholar]
C.J. Chen, Y.Y. Huang, Y.S. Li, Y.C. Chen, C.Y. Chang and Y.M. Huang, Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access 9 (2021) 21986–21997. [Google Scholar]
M.M. Solomon, Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35 (1987) 254–265. [Google Scholar]
X. Wang, S. Poikonen and B. Golden, The vehicle routing problem with drones: several worst-case results. Optim. Lett. 11 (2017) 679–697. [CrossRef] [MathSciNet] [Google Scholar]
L. Di Puglia Pugliese and F. Guerriero, Last-mile deliveries by using drones and classical vehicles, in International Conference on Optimization and Decision Science. Springer, Cham (2017) 557–565. [Google Scholar]
M. Desrochers, J. Desrosiers and M. Solomon, A new optimization algorithm for the vehicle routing problem with time windows. Oper. Res. 40 (1992) 342–354. [Google Scholar]
VRP problem instances, Available at: http://www.branchandcut.org/VRP/data/ (1995). [Google Scholar]
D. Russell, J.J. Coyle, K. Ruamsook and E.A. Thomchick, The real impact of high transportation costs. CSCMP’s Supply Chain Quarterly (2014). [Google Scholar]
J.M. Sanchez-Gomez, M.A. Vega-Rodríguez and C.J. Pérez, An indicator-based multi-objective optimization approach applied to extractive multi-document text summarization. IEEE Latin Am. Trans. 17 (2019) 1291–1299. [Google Scholar]
Y. Ye, Q. Lin, K.C. Wong, J. Li, Z. Ming and C.A.C. Coello, A localized decomposition evolutionary algorithm for imbalanced multi-objective optimization. Eng. App. Artif. Intell. 129 (2024) 107564. [Google Scholar]
S. Sahraei and M. Asadzadeh, Cluster-based multi-objective optimization for identifying diverse design options: application to water resources problems. Environ. Modell. Softw. 135 (2021) 104902. [Google Scholar]
H.T. Kahraman, M. Akbel, S. Duman, M. Kati and H.H. Sayan, Unified space approach-based dynamic switched crowding (DSC): a new method for designing pareto-based multi/many-objective algorithms. Swarm Evol. Comput. 75 (2022) 101196. [Google Scholar]
B.D. Song, K. Park and J. Kim, Persistent UAV delivery logistics: MILP formulation and efficient heuristic. Comput. Ind. Eng. 120 (2018) 418–428. [Google Scholar]
E. Cengiz, C. Yilmaz, H.T. Kahraman and Ç. Suiçmez, Effects of variable UAV speed on optimization of travelling salesman problem with drone (TSP-D), in Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. Springer, Cham (2023) 295–305. [Google Scholar]
I. Barbahan, V. Baikalov, V. Vyatkin and A. Filchenkov, Multi-agent deep reinforcement learning-based algorithm for fast generalization on routing problems. Proc. Comput. Sci. 193 (2021) 228–238. [Google Scholar]
U. Ermağan, B. Yıldız and F.S. Salman, A learning based algorithm for drone routing. Comput. Oper. Res. 137 (2022) 105524. [Google Scholar]
I. Bello, H. Pham, Q.V. Le, M. Norouzi and S. Bengio, Neural combinatorial optimization with reinforcement learning. Preprint arXiv:1611.09940 (2016). [Google Scholar]
E. Khalil, H. Dai, Y. Zhang, B. Dilkina and L. Song, Learning combinatorial optimization algorithms over graphs, in Advances in Neural Information Processing Systems. Vol. 30. (2017). [Google Scholar]
A. Bogyrbayeva, M. Meraliyev, T. Mustakhov and B. Dauletbayev, Learning to solve vehicle routing problems: a survey. IEEE Trans. Intell. Transp. Syst. 25 (2024) 4754–4772. [Google Scholar]
M. Nazari, A. Oroojlooy, L. Snyder and M. Takác, Reinforcement learning for solving the vehicle routing problem. Adv. Neural Inf. Process. Syst. 31 (2018). [Google Scholar]
W. Kool, H. Van Hoof and M. Welling, Attention, learn to solve routing problems! Preprint arXiv:1803.08475 (2018). [Google Scholar]
K. Zhang, F. He, Z. Zhang, X. Lin and M. Li, Multi-vehicle routing problems with soft time windows: a multi-agent reinforcement learning approach. Transp. Res. Part C: Emerg. Technol. 121 (2020) 102861. [Google Scholar]
A. Bogyrbayeva, S. Jang, A. Shah, Y.J. Jang and C. Kwon, A reinforcement learning approach for rebalancing electric vehicle sharing systems. IEEE Trans. Intell. Transp. Syst. 23 (2021) 8704–8714. [Google Scholar]
A. Bogyrbayeva, T. Yoon, H. Ko, S. Lim, H. Yun and C. Kwon, A deep reinforcement learning approach for solving the traveling salesman problem with drone. Transp. Res. Part C: Emerg. Technol. 148 (2023) 103981. [Google Scholar]
M. Lai, M. Battarra, M. Di Francesco and P. Zuddas, An adaptive guidance meta-heuristic for the vehicle routing problem with splits and clustered backhauls. J. Oper. Res. Soc. 66 (2015) 1222–1235. [Google Scholar]
H.T. Kahraman, S. Aras and E. Gedikli, Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowl.-Based Syst. 190 (2020) 105169. [Google Scholar]
F.S. Gharehchopogh, An improved tunicate swarm algorithm with best-random mutation strategy for global optimization problems. J. Bionic Eng. 19 (2022) 1177–1202. [Google Scholar]
B. Ozkaya, H.T. Kahraman, S. Duman and U. Guvenc, Fitness-distance-constraint (FDC) based guide selection method for constrained optimization problems. Appl. Soft Comput. 144 (2023) 110479. [Google Scholar]
C. Yılmaz, E. Cengiz and H.T. Kahraman, A new evolutionary optimization algorithm with hybrid guidance mechanism for truck-multi drone delivery system. Expert Syst. Appl. 245 (2024) 123115. [Google Scholar]
A. Engau and D. Sigler, Pareto solutions in multicriteria optimization under uncertainty. Eur. J. Oper. Res. 281 (2020) 357–368. [Google Scholar]
S. Petchrompo, D.W. Coit, A. Brintrup, A. Wannakrairot and A.K. Parlikad, A review of pareto pruning methods for multi-objective optimization. Comput. Ind. Eng. 167 (2022) 108022. [Google Scholar]
L.D.P. Pugliese, F. Guerriero and G. Macrina, Using drones for parcels delivery process. Proc. Manuf. 42 (2020) 488–497. [Google Scholar]
L.C. Montanã, L. Malagon-Alvarado, P.A. Miranda, M.M. Arboleda, E.L. Solano-Charris and C.A. Vega-Mejía, A novel mathematical approach for the truck-and-drone location-routing problem. Proc. Comput. Sci. 200 (2022) 1378–1391. [Google Scholar]
H. Duan, G. Zhang, S. Wang and Y. Fan, Integrated benefit-cost analysis of China’s optimal adaptation and targeted mitigation. Ecol. Econ. 160 (2019) 76–86. [Google Scholar]
H. Duan, G. Zhang, S. Wang and Y. Fan, Robust climate change research: a review on multi-model analysis. Environ. Res. Lett. 14 (2019) 033001. [Google Scholar]

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