Urban flooding increasingly impairs road network capacity and delays ground-based emergency rescue operations. To address the fundamental conflict between dynamic road obstructions and rescue urgency, we proposed a novel coordinated rescue framework integrating emergency vehicles and drones. A multi-objective mixed-integer programming model was formulated to minimize total rescue time, total cost, and average time-window penalty. The model incorporated a vehicle speed function governed by real-time inundation depth and a time-varying drone ground speed model based on wind speed and direction, collectively capturing the dynamic operational environment. Meteorological risks were further quantified within the collaborative path optimization module, with drone flight safety guaranteed through cumulative risk thresholds. To solve this NP-hard problem, an enhanced adaptive large neighborhood search (EALNS) algorithm was developed, integrating a predictive search mechanism, problem-oriented operator design, and local search enhancement strategies to improve solution efficiency and quality in complex dynamic environments. The validity of the proposed model and algorithm was assessed through numerical experiments based on multiple benchmark instances and a real-world case study from Tianjin, China. Our experimental results demonstrated that EALNS achieved average improvements of 45%, 10.9%, and 37.5% over the genetic algorithm, the standard adaptive large neighborhood search algorithm, and the whale optimization algorithm, respectively. Furthermore, compared with the vehicle-only mode, the collaborative rescue mode reduced total rescue time, total cost, and average time-window penalty by 11.2%, 10.0%, and 39.4%, respectively. These results collectively validate the effectiveness and practical applicability of the proposed approach under dynamic disaster conditions.
Citation: Yuanbo Zhang, Na Li. Collaborative route optimization for urban emergency vehicles and drones under dynamic evolution of heavy rainfall-induced urban flooding[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3789-3834. doi: 10.3934/jimo.2026136
Urban flooding increasingly impairs road network capacity and delays ground-based emergency rescue operations. To address the fundamental conflict between dynamic road obstructions and rescue urgency, we proposed a novel coordinated rescue framework integrating emergency vehicles and drones. A multi-objective mixed-integer programming model was formulated to minimize total rescue time, total cost, and average time-window penalty. The model incorporated a vehicle speed function governed by real-time inundation depth and a time-varying drone ground speed model based on wind speed and direction, collectively capturing the dynamic operational environment. Meteorological risks were further quantified within the collaborative path optimization module, with drone flight safety guaranteed through cumulative risk thresholds. To solve this NP-hard problem, an enhanced adaptive large neighborhood search (EALNS) algorithm was developed, integrating a predictive search mechanism, problem-oriented operator design, and local search enhancement strategies to improve solution efficiency and quality in complex dynamic environments. The validity of the proposed model and algorithm was assessed through numerical experiments based on multiple benchmark instances and a real-world case study from Tianjin, China. Our experimental results demonstrated that EALNS achieved average improvements of 45%, 10.9%, and 37.5% over the genetic algorithm, the standard adaptive large neighborhood search algorithm, and the whale optimization algorithm, respectively. Furthermore, compared with the vehicle-only mode, the collaborative rescue mode reduced total rescue time, total cost, and average time-window penalty by 11.2%, 10.0%, and 39.4%, respectively. These results collectively validate the effectiveness and practical applicability of the proposed approach under dynamic disaster conditions.
| [1] |
J. Chen, X. Shi, L. Gu, G. Wu, T. Su, H. M. Wang, et al., Impacts of climate warming on global floods and their implication to current flood defense standards, J. Hydrol., 618 (2023), 129236. https://doi.org/10.1016/j.jhydrol.2023.129236 doi: 10.1016/j.jhydrol.2023.129236
|
| [2] |
B. F. Sanders, J. E. Schubert, D. T. Kahl, K. J. Mach, D. Brady, A. AghaKouchak, et al., Large and inequitable flood risks in Los Angeles, California, Nat. Sustain., 6 (2023), 47–57. https://doi.org/10.1038/s41893-022-00977-7 doi: 10.1038/s41893-022-00977-7
|
| [3] |
M. Yan, A. Wilson, J. L. Peel, S. Magzamen, Q. Sun, T. Li, et al., Community-wide Mortality Rates in Beijing, China, During the July 2012 Flood Compared with Unexposed Periods, Epidemiology., 31 (2020), 319–326. https://doi.org/10.1097/EDE.0000000000001182 doi: 10.1097/EDE.0000000000001182
|
| [4] |
J. C. L. Normand, E. Heggy, Assessing flash flood erosion following storm Daniel in Libya, Nat. Commun., 15 (2024), 6493. https://doi.org/10.1038/s41467-024-49699-8 doi: 10.1038/s41467-024-49699-8
|
| [5] |
K. T. C. Lim Kam Sian, P. Sagero, L. L. Kebacho, V. Ongoma, Dubai's record precipitation event of 16 April 2024–A diagnosis, Atmos. Res., 315 (2025), 107924. https://doi.org/10.1016/j.atmosres.2025.107924 doi: 10.1016/j.atmosres.2025.107924
|
| [6] |
Y. He, D. Ma, J. Xiong, W. Cheng, H. Jia, N. Wang, et al., Flash flood vulnerability assessment of roads in China based on support vector machine, Geocarto Int., 37 (2022), 6141–6164. https://doi.org/10.1080/10106049.2021.1926560 doi: 10.1080/10106049.2021.1926560
|
| [7] |
E. E. Koks, J. Rozenberg, C. Zorn, M. Tariverdi, M. Vousdoukas, S. A. Fraser, et al., A global multi-hazard risk analysis of road and railway infrastructure assets, Nat. Commun., 10 (2019), 2677. https://doi.org/10.1038/s41467-019-10442-3 doi: 10.1038/s41467-019-10442-3
|
| [8] |
P. Singh, V. S. P. Sinha, A. Vijhani, N. Pahuja, Vulnerability assessment of urban road network from urban flood, Int. J. Disaster Risk Reduct., 28 (2018), 237–250. https://doi.org/10.1016/j.ijdrr.2018.03.017 doi: 10.1016/j.ijdrr.2018.03.017
|
| [9] |
M. K. Banafaa, Ö. Pepeoğlu, I. Shayea, A. Alhammadi, Z. A. Shamsan, M. A. Razaz, et al., A Comprehensive Survey on 5G-and-Beyond Networks With UAVs: Applications, Emerging Technologies, Regulatory Aspects, Research Trends and Challenges, IEEE Access., 12 (2024), 7786–7826. https://doi.org/10.1109/ACCESS.2023.3349208 doi: 10.1109/ACCESS.2023.3349208
|
| [10] |
S. A. H. Mohsan, M. A. Khan, F. Noor, I. Ullah, M. H. Alsharif, Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review, Drones, 6 (2022), 147. https://doi.org/10.3390/drones6060147 doi: 10.3390/drones6060147
|
| [11] |
T. I. Faiz, C. Vogiatzis, Md. Noor-E-Alam, Computational approaches for solving two-echelon vehicle and UAV routing problems for post-disaster humanitarian operations, Expert Syst. Appl., 237 (2024), 121473. https://doi.org/10.1016/j.eswa.2023.121473 doi: 10.1016/j.eswa.2023.121473
|
| [12] |
M. Daneshvar, S. D. Jena, W. Rei, A two-stage stochastic post-disaster humanitarian supply chain network design problem, Comput. Ind. Eng., 183 (2023), 109459. https://doi.org/10.1016/j.cie.2023.109459 doi: 10.1016/j.cie.2023.109459
|
| [13] |
R. Van Steenbergen, M. Mes, W. Van Heeswijk, Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty, Transp. Res. Part C Emerg. Technol., 157 (2023), 104401. https://doi.org/10.1016/j.trc.2023.104401 doi: 10.1016/j.trc.2023.104401
|
| [14] |
X. Wang, T. M. Choi, H. Liu, X. Yue, A Novel Hybrid Ant Colony Optimization Algorithm for Emergency Transportation Problems During Post-Disaster Scenarios, IEEE Trans. Syst. Man Cybern, Syst., 48 (2018), 545–556. https://doi.org/10.1109/TSMC.2016.2606440 doi: 10.1109/TSMC.2016.2606440
|
| [15] |
M. Lyu, Y. Zhao, C. Huang, H. Huang, Unmanned Aerial Vehicles for Search and Rescue: A Survey, Remote Sens., 15 (2023), 3266. https://doi.org/10.3390/rs15133266 doi: 10.3390/rs15133266
|
| [16] |
M. Kucharczyk, C. H. Hugenholtz, Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities, Remote Sens. Environ., 264 (2021), 112577. https://doi.org/10.1016/j.rse.2021.112577 doi: 10.1016/j.rse.2021.112577
|
| [17] |
G. Zhang, N. Jia, N. Zhu, Y. Adulyasak, S. Ma, Robust drone selective routing in humanitarian transportation network assessment, Eur. J. Oper. Res., 305 (2023), 400–428. https://doi.org/10.1016/j.ejor.2022.05.046 doi: 10.1016/j.ejor.2022.05.046
|
| [18] |
G. Macrina, L. Di Puglia Pugliese, F. Guerriero, G. Laporte, Drone-aided routing: A literature review, Transp. Res. Part C Emerg. Technol., 120 (2020), 102762. https://doi.org/10.1016/j.trc.2020.102762 doi: 10.1016/j.trc.2020.102762
|
| [19] |
L. Du, X. Li, Y. Gan, K. Leng, Optimal Model and Algorithm of Medical Materials Delivery Drone Routing Problem under Major Public Health Emergencies, Sustainability, 14 (2022), 4651. https://doi.org/10.3390/su14084651 doi: 10.3390/su14084651
|
| [20] |
Z. Ghelichi, M. Gentili, P. B. Mirchandani, Logistics for a fleet of drones for medical item delivery: A case study for Louisville, KY, Comput. Oper. Res., 135 (2021), 105443. https://doi.org/10.1016/j.cor.2021.105443 doi: 10.1016/j.cor.2021.105443
|
| [21] |
P. Kumar, K. Pal, M. C. Govil, Comprehensive Review of Path Planning Techniques for Unmanned Aerial Vehicles (UAVs), ACM Comput. Surv., 58 (2026), 1–44. https://doi.org/10.1145/3737280 doi: 10.1145/3737280
|
| [22] |
C. C. Murray, 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. https://doi.org/10.1016/j.trc.2015.03.005 doi: 10.1016/j.trc.2015.03.005
|
| [23] |
G. Wu, N. Mao, Q. Luo, B. Xu, J. Shi, P. N. Suganthan, Collaborative Truck-Drone Routing for Contactless Parcel Delivery During the Epidemic, IEEE Trans. Intell. Transport. Syst., 23 (2022), 25077–25091. https://doi.org/10.1109/TITS.2022.3181282 doi: 10.1109/TITS.2022.3181282
|
| [24] |
S. H. Huang, Y. H. Huang, C. A. Blazquez, C. Y. Chen, Solving the vehicle routing problem with drone for delivery services using an ant colony optimization algorithm, Adv. Eng. Inform., 51 (2022), 101536. https://doi.org/10.1016/j.aei.2022.101536 doi: 10.1016/j.aei.2022.101536
|
| [25] |
R. G. Ribeiro, L. P. Cota, T. A. M. Euzebio, J. A. Ramirez, F. G. Guimaraes, Unmanned-Aerial-Vehicle Routing Problem With Mobile Charging Stations for Assisting Search and Rescue Missions in Postdisaster Scenarios, IEEE Trans. Syst. Man Cybern, Syst., 52 (2022), 6682–6696. https://doi.org/10.1109/TSMC.2021.3088776 doi: 10.1109/TSMC.2021.3088776
|
| [26] |
W. Peng, D. Wang, Y. Yin, T. C. E. Cheng, Multi-agent deep reinforcement learning-based truck-drone collaborative routing with dynamic emergency response, Transp. Res. Part E Logist. Transp. Rev., 195 (2025), 103974. https://doi.org/10.1016/j.tre.2025.103974 doi: 10.1016/j.tre.2025.103974
|
| [27] |
C. C. Murray, R. Raj, The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones, Transp. Res. Part C Emerg. Technol., 110 (2020), 368–398. https://doi.org/10.1016/j.trc.2019.11.003 doi: 10.1016/j.trc.2019.11.003
|
| [28] |
M. R. Salama, S. Srinivas, Collaborative truck multi-drone routing and scheduling problem: Package delivery with flexible launch and recovery sites, Transp. Res. Part E Logist. Transp. Rev., 164 (2022), 102788. https://doi.org/10.1016/j.tre.2022.102788 doi: 10.1016/j.tre.2022.102788
|
| [29] |
T. Thomas, S. Srinivas, C. Rajendran, Collaborative truck multi-drone delivery system considering drone scheduling and en route operations, Ann. Oper. Res., 339 (2024), 693–739. https://doi.org/10.1007/s10479-023-05418-y doi: 10.1007/s10479-023-05418-y
|
| [30] |
H. Zhou, H. Qin, C. Cheng, L. M. Rousseau, An exact algorithm for the two-echelon vehicle routing problem with drones, Transp. Res. Part B Methodol., 168 (2023), 124–150. https://doi.org/10.1016/j.trb.2023.01.002 doi: 10.1016/j.trb.2023.01.002
|
| [31] |
O. Dukkanci, A. Koberstein, B. Y. Kara, Drones for relief logistics under uncertainty after an earthquake, Eur. J. Oper. Res., 310 (2023), 117–132. https://doi.org/10.1016/j.ejor.2023.02.038 doi: 10.1016/j.ejor.2023.02.038
|
| [32] |
Y. Gong, W. Wang, Y. Zhou, J. Cheng, Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination, J. Flood Risk Manag., 18 (2025), e13045. https://doi.org/10.1111/jfr3.13045 doi: 10.1111/jfr3.13045
|
| [33] |
R. Arumugaraj Nadar, J. K. Jha, J. J. Thakkar, Performance Evaluation of Emergency Medical Service Systems with Multiple Ambulance Types and Patient Types, Int. J. Ind. Eng. Theory., 31 (2024). https://doi.org/10.23055/IJIETAP.2024.31.4.9069 doi: 10.23055/IJIETAP.2024.31.4.9069
|
| [34] |
I. Acar, I. Altin, Multi-Depot General Colored Traveling Salesman Problem with Time Windows in Home Healthcare System: A Medication Delivery Example, Int. J. Ind. Eng. Theory., 32 (2025). https://doi.org/10.23055/IJIETAP.2025.32.2.10657 doi: 10.23055/IJIETAP.2025.32.2.10657
|
| [35] |
Z. Luo, M. Poon, Z. Zhang, Z. Liu, A. Lim, The Multi-visit Traveling Salesman Problem with Multi-Drones, Transp. Res. Part C Emerg. Technol., 128 (2021), 103172. https://doi.org/10.1016/j.trc.2021.103172 doi: 10.1016/j.trc.2021.103172
|
| [36] |
R. Gu, M. Poon, Z. Luo, Y. Liu, 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. https://doi.org/10.1016/j.trc.2022.103733 doi: 10.1016/j.trc.2022.103733
|
| [37] |
S. Zhong, R. Cheng, Y. Jiang, Z. Wang, A. Larsen, O. A. Nielsen, Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand, Transp. Res. Part E Logist. Transp. Rev., 141 (2020), 102015. https://doi.org/10.1016/j.tre.2020.102015 doi: 10.1016/j.tre.2020.102015
|
| [38] |
C. Cao, Y. Liu, O. Tang, X. Gao, A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains, Int. J. Prod. Econ., 235 (2021), 108081. https://doi.org/10.1016/j.ijpe.2021.108081 doi: 10.1016/j.ijpe.2021.108081
|
| [39] |
J. Zhao, Y. Guo, X. Duan, Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction, J. Adv. Transp., 2017 (2017), 1–14. https://doi.org/10.1155/2017/9184891 doi: 10.1155/2017/9184891
|
| [40] |
J. F. Ehmke, A. M. Campbell, B. W. Thomas, Vehicle routing to minimize time-dependent emissions in urban areas, Eur. J. Oper. Res., 251 (2016), 478–494. https://doi.org/10.1016/j.ejor.2015.11.034 doi: 10.1016/j.ejor.2015.11.034
|
| [41] |
K. Liu, L. Yang, Y. Zhao, Z. H. Zhang, Multi-period stochastic programming for relief delivery considering evolving transportation network and temporary facility relocation/closure, Transp. Res. Part E Logist. Transp. Rev., 180 (2023), 103357. https://doi.org/10.1016/j.tre.2023.103357 doi: 10.1016/j.tre.2023.103357
|
| [42] |
F. Avishan, M. Elyasi, İ. Yanıkoğlu, A. Ekici, O. Ö. Özener, Humanitarian Relief Distribution Problem: An Adjustable Robust Optimization Approach, Transp. Sci., 57 (2023), 1096–1114. https://doi.org/10.1287/trsc.2023.1204 doi: 10.1287/trsc.2023.1204
|
| [43] |
X. Yang, W. Cao, K. Wang, H. Yin, J. Wu, L. Wu, Integrated scheduling of truck and drone fleets for cargo transportation in post-disaster relief: A two-stage stochastic optimization approach, Transp. Res. Part E Logist. Transp. Rev., 196 (2025), 104015. https://doi.org/10.1016/j.tre.2025.104015 doi: 10.1016/j.tre.2025.104015
|
| [44] |
K. Li, X. Yan, Y. Han, Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field, Appl. Soft Comput., 150 (2024), 111033. https://doi.org/10.1016/j.asoc.2023.111033 doi: 10.1016/j.asoc.2023.111033
|
| [45] |
D. Chen, C. Imdahl, D. Lai, T. Van Woensel, A reinforcement learning approach for the dynamic vehicle routing and scheduling problem with stochastic request times and time-dependent, stochastic travel times, Transp. Res. Part C Emerg. Technol., 182 (2026), 105387. https://doi.org/10.1016/j.trc.2025.105387 doi: 10.1016/j.trc.2025.105387
|
| [46] |
K. Li, H. Cui, Q. Meng, X. Zhang, Dynamic truck-drone collaborative transportation during hurricanes, Transp. Res. Part C Emerg. Technol., 180 (2025) 105322. https://doi.org/10.1016/j.trc.2025.105322 doi: 10.1016/j.trc.2025.105322
|
| [47] |
Y. Shi, K. Zhang, J. Zhang, M. Chen, J. Yang, H. Guo, Dynamic truck-drone cooperative delivery of emergency supplies considering secondary disasters, Transp. Res. Part E Logist. Transp. Rev., 206 (2026), 104543. https://doi.org/10.1016/j.tre.2025.104543 doi: 10.1016/j.tre.2025.104543
|
| [48] |
L. Costa, C. Contardo, G. Desaulniers, Exact Branch-Price-and-Cut Algorithms for Vehicle Routing, Transp. Sci., 53 (2019), 946–985. https://doi.org/10.1287/trsc.2018.0878 doi: 10.1287/trsc.2018.0878
|
| [49] |
S. A. Vásquez, G. Angulo, M. A. Klapp, An exact solution method for the TSP with Drone based on decomposition, Comput. Oper. Res., 127 (2021), 105127. https://doi.org/10.1016/j.cor.2020.105127 doi: 10.1016/j.cor.2020.105127
|
| [50] |
M. F. Saeed Osman, Dynamic Computational Approach for Routing and Scheduling Last-Mile Distribution, Int. J. Ind. Eng. Theory., 32 (2025). https://doi.org/10.23055/IJIETAP.2025.32.6.11095 doi: 10.23055/IJIETAP.2025.32.6.11095
|
| [51] |
A. Motaghedi-Larijani, Solving the number of cross-dock open doors optimization problem by combination of NSGA-Ⅱ and multi-objective simulated annealing, Appl. Soft Comput., 128 (2022), 109448. https://doi.org/10.1016/j.asoc.2022.109448 doi: 10.1016/j.asoc.2022.109448
|
| [52] |
Y. Li, Z. Zhang, Q. Sun, Y. Huang, An improved ant colony algorithm for multiple unmanned aerial vehicles route planning, J. Frankl. Inst., 361 (2024), 107060. https://doi.org/10.1016/j.jfranklin.2024.107060 doi: 10.1016/j.jfranklin.2024.107060
|
| [53] |
K. Peng, J. Du, F. Lu, Q. Sun, Y. Dong, P. Zhou, et al., A Hybrid Genetic Algorithm on Routing and Scheduling for Vehicle-Assisted Multi-Drone Parcel Delivery, IEEE Access, 7 (2019), 49191–49200. https://doi.org/10.1109/ACCESS.2019.2910134 doi: 10.1109/ACCESS.2019.2910134
|
| [54] |
S. S. Özarık, V. Lurkin, L. P. Veelenturf, T. Van Woensel, G. Laporte, An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits, Transp. Res. Part B Methodol., 170 (2023), 194–220. https://doi.org/10.1016/j.trb.2023.02.016 doi: 10.1016/j.trb.2023.02.016
|
| [55] |
S. Meng, Y. Chen, D. Li, The multi-visit drone-assisted pickup and delivery problem with time windows, Eur. J. Oper. Res., 314 (2024), 685–702. https://doi.org/10.1016/j.ejor.2023.10.021 doi: 10.1016/j.ejor.2023.10.021
|
| [56] |
J. Cheng, Y. Zhou, C. Wu, Z. Li, Collaborative truck–drone–motorcycle delivery of emergency supplies for mountain wildfire suppression, Comput. Ind. Eng., 196 (2024), 110468. https://doi.org/10.1016/j.cie.2024.110468 doi: 10.1016/j.cie.2024.110468
|
| [57] |
K. Li, T. Zhang, R. Wang, Deep Reinforcement Learning for Multiobjective Optimization, IEEE Trans. Cybern., 51 (2021), 3103–3114. https://doi.org/10.1109/TCYB.2020.2977661 doi: 10.1109/TCYB.2020.2977661
|
| [58] |
Q. Guan, H. Cao, L. Jia, D. Yan, B. Chen, Synergetic attention-driven transformer: A deep reinforcement learning approach for vehicle routing problems, Expert Syst. Appl., 274 (2025), 126961. https://doi.org/10.1016/j.eswa.2025.126961 doi: 10.1016/j.eswa.2025.126961
|
| [59] |
F. Akkerman, M. Mes, W. Van Jaarsveld, A comparison of reinforcement learning policies for dynamic vehicle routing problems with stochastic customer requests, Comput. Ind. Eng., 200 (2025), 110747. https://doi.org/10.1016/j.cie.2024.110747 doi: 10.1016/j.cie.2024.110747
|
| [60] |
Y. Zhou, W. Yang, Y. Gong, Reinforcement learning with priority decentralized PPO for multi-vessel cooperative rescue scheduling in flood disaster, Alexandria Eng. J., 138 (2026), 96–113. https://doi.org/10.1016/j.aej.2026.01.047 doi: 10.1016/j.aej.2026.01.047
|
| [61] | L. Du, X. K. Yang, Study on the impact of traffic and loss caused by different inundation depth, Technological Innovation, Green Transportation: Proceedings of the 11th National Urban Road Transportation Academic Conference, Dalian: China Civil Engineering Society, 5 (2011), 389–393. |
| [62] |
A. Thibbotuwawa, G. Bocewicz, G. Radzki, P. Nielsen, Z. Banaszak, UAV Mission Planning Resistant to Weather Uncertainty, Sensors, 20 (2020), 515. https://doi.org/10.3390/s20020515 doi: 10.3390/s20020515
|
| [63] |
M. Tsang, D. M. Scott, An integrated approach to modeling the impact of floods on emergency services: A case study of Calgary, Alberta, J. Transp. Geogr., 86 (2020), 102774. https://doi.org/10.1016/j.jtrangeo.2020.102774 doi: 10.1016/j.jtrangeo.2020.102774
|
| [64] |
E. Al-Qadami, M. A. M. Razi, S. M. H. Shah, J. H. Pu, M. Amran, M. A. Díaz Huenchuan, et al., Floodwaters and vehicle hydrodynamics: A deep dive into risk mitigation unraveling vehicle stability in floods, Results Eng., 23 (2024), 102540. https://doi.org/10.1016/j.rineng.2024.102540 doi: 10.1016/j.rineng.2024.102540
|
| [65] |
N. Agatz, P. Bouman, M. Schmidt, Optimization Approaches for the Traveling Salesman Problem with Drone, Transp. Sci., 52 (2018), 965–981. https://doi.org/10.1287/trsc.2017.0791 doi: 10.1287/trsc.2017.0791
|
| [66] |
L. Zhen, J. Gao, Z. Tan, S. Wang, R. Baldacci, Branch-price-and-cut for trucks and drones cooperative delivery, IISE Trans., 55 (2023), 271–287. https://doi.org/10.1080/24725854.2022.2060535 doi: 10.1080/24725854.2022.2060535
|
| [67] |
J. Gao, L. Zhen, G. Laporte, X. He, Scheduling trucks and drones for cooperative deliveries, Transp. Res. Part E Logist. Transp. Rev., 178 (2023), 103267. https://doi.org/10.1016/j.tre.2023.103267 doi: 10.1016/j.tre.2023.103267
|
| [68] |
C. Liu, Z. Wu, Y. Zhou, L. Luo, X. Zhou, X. Xie, The optimization of dynamic and cooperative truck-drone distribution routes for emergency supplies delivery in pandemic areas, J. Syst. Sci. Math. Sci., 42 (2022), 3027–3043. https://doi.org/10.12341/jssms22266 doi: 10.12341/jssms22266
|
| [69] |
X. Wang, L. Zhan, Y. Zhang, T. Fei, M. L. Tseng, Environmental cold chain distribution center location model in the semiconductor supply chain: A hybrid arithmetic whale optimization algorithm, Comput. Ind. Eng., 187 (2024), 109773. https://doi.org/10.1016/j.cie.2023.109773 doi: 10.1016/j.cie.2023.109773
|
| [70] |
M. Keskin, B. Çatay, Partial recharge strategies for the electric vehicle routing problem with time windows, Transp. Res. Part C Emerg. Technol., 65 (2016), 111–127. https://doi.org/10.1016/j.trc.2016.01.013 doi: 10.1016/j.trc.2016.01.013
|
| [71] |
J. Lehmann, M. Winkenbach, A matheuristic for the Two-Echelon Multi-Trip Vehicle Routing Problem with mixed pickup and delivery demand and time windows, Transp. Res. Part C Emerg. Technol., 160 (2024), 104522. https://doi.org/10.1016/j.trc.2024.104522 doi: 10.1016/j.trc.2024.104522
|
| [72] |
M. Liu, Y. Li, X. Wang, Joint optimization of truck-drone routing for last-mile deliveries in urban areas, Transportmetrica A: Transp. Sci., (2024), 1–27. https://doi.org/10.1080/23249935.2024.2392611 doi: 10.1080/23249935.2024.2392611
|
Figures(6) / Tables(13)
Yuanbo Zhang, Na Li. Collaborative route optimization for urban emergency vehicles and drones under dynamic evolution of heavy rainfall-induced urban flooding[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3789-3834. doi: 10.3934/jimo.2026136
DownLoad: