Volume 26 Issue 6
Jun.  2026
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2026  >  26(6): 239-256
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LI Jia-cheng, WU Di, WANG Feng, LIU Bao-li, ZHENG Jian-feng. Location-routing optimization for joint land-sea emergency delivery to large islands[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 239-256. doi: 10.19818/j.cnki.1671-1637.2026.119
Citation: LI Jia-cheng, WU Di, WANG Feng, LIU Bao-li, ZHENG Jian-feng. Location-routing optimization for joint land-sea emergency delivery to large islands[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 239-256. doi: 10.19818/j.cnki.1671-1637.2026.119
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Location-routing optimization for joint land-sea emergency delivery to large islands

doi: 10.19818/j.cnki.1671-1637.2026.119
  • 1.

    College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

  • 2.

    Ship Design Department 3, Shanghai Merchant Ship Design & Research Institute, Shanghai 201203, China

Funds:

National Natural Science Foundation of China 72104042

National Natural Science Foundation of China 72301051

More Information
  • Corresponding author: WU Di, associate professor, PhD, E-mail: wudidlmu@163.com
  • Received Date: 2025-04-22
  • Accepted Date: 2025-11-27
  • Rev Recd Date: 2025-09-22
  • Publish Date: 2026-06-28
    Abstract
  • Considering some real-world factors, such as material priority, fleet heterogeneity, and the personnel/material capacities of assembly ports and vessels, a location-routing optimization model was constructed. This model employed assembly port location, vessel voyage scheduling, and route configuration as variables, aiming to minimize delivery time. An integrated optimization algorithm was proposed. It leveraged an adaptive large neighborhood search algorithm as the framework for the outer-layer optimization loop. An improved simulated annealing algorithm was embedded as the inner-layer optimization module. The integration of global and local search was achieved through an interaction mechanism between the inner and outer layers. The effectiveness of the proposed model and algorithm was validated through an empirical case study of emergency delivery to China's South China Sea region. Research results show that the optimized delivery time is reduced from 78.52 h to 48.03 h, showing an efficiency improvement of 6.54%-48.51% and a stability enhancement of 10.77%-72.92% in comparison with existing algorithms. Further sensitivity analysis reveals that vessel capacity, speed, the number of assembly ports, and the average speed of ground transportation are all negatively correlated with delivery time of the system, while delivery personnel and material quantities, the number of landing ports are all positively correlated with delivery time of the system, exhibiting diminishing marginal effects. Deploying small or slow vessels can significantly undermine system performance. The proposed algorithm has the capability of effectively balancing material priority conflicts and heterogeneous transportation resource constraints. It provides timely and robust decision support for joint land-sea emergency delivery, and offers theoretical implications for extending the research on location-routing problems in special scenarios.

     

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    • References(40)
  • [1]
    HAI Jun. An exploration into the modes of the strategic projection of the army[J]. Traffic Engineering and Technology for National Defense, 2013, 11(5): 8-10, 47.
    [2]
    ZHU Guang, LIU Yong-hua. Construction of strategic projection capability for dealing with major unexpected incidents[J]. Journal of Academy of Military Transportation, 2016, 18(1): 1-5.
    [3]
    BAO Li-ping, BU Chao, LU Zheng-sheng. Countermeasures for accelerating strategic projection capability generation under new situation[J]. Journal of Military Transportation University, 2017, 19(1): 1-4.
    [4]
    SUN Hua-li, LI Ze-ping, MA Teng. Robust optimization of joint road restoration and emergency location-routing[J]. Systems Engineering - Theory & Practice, 2023, 43(9): 2701-2716.
    [5]
    ZHU Li, CAO Jie, GU Jun, et al. Dynamic emergency supply distribution considering fair mitigation of victim suffering[J]. Systems Engineering - Theory and Practice, 2020, 40(9): 2427-2437.
    [6]
    GUO Peng-hui, ZHU Jian-jun, WANG He-hua. Location-routing-allocation problem with consolidated shipping of heterogeneous relief supplies in post-disaster rescue[J]. Systems Engineering - Theory and Practice, 2019, 39(9): 2345-2360.
    [7]
    WANG Xu-ping, MA Chao, RUAN Jun-hu. Model and algorithm of relief materials dynamic scheduling without sufficient vehicle quantity[J]. Systems Engineering - Theory and Practice, 2013, 33(6): 1492-1500.
    [8]
    YU Dong-mei, GAO Lei-fu, ZHAO Shi-jie. Emergency facility location-allocation problem with convex barriers[J]. Systems Engineering - Theory and Practice, 2019, 39(5): 1178-1188.
    [9]
    YANG Xiao-xia, ZHANG Rui, LI Yong-Hang, et al. A multi-objective route optimization method for passenger evacuations at subway stations during a fire outbreak[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 192-209. doi: 10.19818/j.cnki.1671-1637.2023.05.013
    [10]
    MA Chang-xi, SHI Chu-wei, DU Bo. Hub-and-spoke emergency rescue network planning[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 198-208. doi: 10.19818/j.cnki.1671-1637.2023.03.015
    [11]
    ZHAO Jian-you, HAN Wan-li, ZHENG Wen-jie, et al. Distribution of emergency medical supplies in cities under major public health emergency[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 168-177.
    [12]
    SHEU J B. Dynamic relief-demand management for emergency logistics operations under large-scale disasters[J]. Transportation Research Part E: Logistics and Transportation Review, 2010, 46(1): 1-17. doi: 10.1016/j.tre.2009.07.005
    [13]
    SAFAEI A S, FARSAD S, PAYDAR M M. Emergency logistics planning under supply risk and demand uncertainty[J]. Operational Research, 2020, 20(3): 1437-1460. doi: 10.1007/s12351-018-0376-3
    [14]
    ZAHEDI A, KARGARI M, HUSSEINZADEH KASHAN A. Multi-objective decision-making model for distribution planning of goods and routing of vehicles in emergency multi-objective decision-making model for distribution planning of goods and routing of vehicles in emergency[J]. International Journal of Disaster Risk Reduction, 2020, 48: 101587. doi: 10.1016/j.ijdrr.2020.101587
    [15]
    SANCI E C, DASKIN M S. An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief[J]. Transportation Research Part B: Methodological, 2021, 145: 152-184. doi: 10.1016/j.trb.2021.01.005
    [16]
    HUANG K, JIANG Y P, YUAN Y F, et al. Modeling multiple humanitarian objectives in emergency response to large-scale disasters[J]. Transportation Research Part E: Logistics and Transportation Review, 2015, 75: 1-17.
    [17]
    CAMUR M C, SHARKEY T C, DORSEY C, et al. Optimizing the response for Arctic mass rescue events[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 152: 102368. doi: 10.1016/j.tre.2021.102368
    [18]
    AKHLAGHI V E, CAMPBELL A M, DE MATTA R E. Fuel distribution planning for disasters: Models and case study for Puerto Rico[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 152: 102403. doi: 10.1016/j.tre.2021.102403
    [19]
    WANG Y, ASSOGBA K, LIU Y, et al. Two-echelon location-routing optimization with time windows based on customer clustering[J]. Expert Systems with Applications, 2018, 104: 244-260. doi: 10.1016/j.eswa.2018.03.018
    [20]
    WU Di, ZHU Yu-xi, WU Wen-long, et al. Location-routing optimization for regular maritime cruise and emergency rescue system in remote is lands[J]. Journal of Traffic and Transportation Engineering, 2025, 25(6): 200-218. doi: 10.19818/j.cnki.1671-1637.2025.06.017
    [21]
    BOCCIA M, CRAINIC T G, SFORZA A, et al. Multi-commodity location-routing: Flow intercepting formulation and branch-and-cut algorithm[J]. Computers and Operations Research, 2018, 89: 94-112. doi: 10.1016/j.cor.2017.08.013
    [22]
    WANG M T, ZHANG C R, BELL M G H, et al. A branch-and-price algorithm for location-routing problems with pick-up stations in the last-mile distribution system[J]. European Journal of Operational Research, 2022, 303(3): 1258-1276. doi: 10.1016/j.ejor.2022.03.058
    [23]
    LOPES R B, FERREIRA C, SANTOS B S. A simple and effective evolutionary algorithm for the capacitated location-routing problem[J]. Computers and Operations Research, 2016, 70: 155-162. doi: 10.1016/j.cor.2016.01.006
    [24]
    WANG H J, DU L J, MA S H. Multi-objective open location -routing model with split delivery for optimized relief distribution in post-earthquake[J]. Transportation Research Part E: Logistics and Transportation Review, 2014, 69: 160-179. doi: 10.1016/j.tre.2014.06.006
    [25]
    LI Ya, WANG Xu-ping, LIN Na, et al. The location-routing optimization model and algorithm of multi-type precooling facilities for agricultural products[J]. Systems Engineering -Theory and Practice, 2022, 42(11): 3016-3029.
    [26]
    MA Yan-fang, YING Bin, ZHOU Xiao-yang, et al. Multi-agent optimization model and algorithm for perishable food location-routing problem with conflict and coordination[J]. Systems Engineering - Theory and Practice, 2020, 40(12): 3194-3209.
    [27]
    HUANG Kai-ming, LU Cai-wu, LIAN Min-jie. Research on modeling and algorithm for three-echelon location-routing problem[J]. Systems Engineering - Theory and Practice, 2018, 38(3): 743-754.
    [28]
    WANG Y, PENG S G, ZHOU X S, et al. Green logistics location-routing problem with eco-packages[J]. Transportation Research Part E: Logistics and Transportation Review, 2020, 143: 102118. doi: 10.1016/j.tre.2020.102118
    [29]
    HOF J, SCHNEIDER M, GOEKE D. Solving the battery swap station location-routing problem with capacitated electric vehicles using an AVNS algorithm for vehicle-routing problems with intermediate stops[J]. Transportation Research Part B: Methodological, 2017, 97: 102-112. doi: 10.1016/j.trb.2016.11.009
    [30]
    LI Hui-fang, HU Da-wei, CHEN Xi-qiong, et al. Expanding hub location-routing problem for hybrid hub-and-spoke multimodal transport network considering carbon emissions[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 306-321. doi: 10.19818/j.cnki.1671-1637.2022.04.024
    [31]
    YANG Zhong-zhen, MU Xue, ZHU Xiao-cong. Optimization model of distribution network with multiple distribution centers and multiple demand points considering traffic flow change[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 100-107. doi: 10.19818/j.cnki.1671-1637.2015.01.013
    [32]
    LIU Xin-meng, HE Shi-wei, CHEN Sheng-bo, et al. Multi-agent evolutionary algorithm of VRP problem with time window[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 105-110.
    [33]
    AKPUNAR Ö Ş, AKPINAR Ş. A hybrid adaptive large neighbourhood search algorithm for the capacitated location routing problem[J]. Expert Systems with Applications, 2021, 168: 114304. doi: 10.1016/j.eswa.2020.114304
    [34]
    WU J B, LI Q H, BIE Y M, et al. Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm[J]. Energy, 2024, 304: 132142. doi: 10.1016/j.energy.2024.132142
    [35]
    IMANI A, KARIMI H, DEIRANLOU M. The bi-objective multi-depot split delivery location routing problems under uncertain conditions[J]. International Journal of Systems Science: Operations and Logistics, 2024, 11: 2322512. doi: 10.1080/23302674.2024.2322512
    [36]
    RAHMANIFAR G, MOHAMMADI M, GOLABIAN M, et al. Integrated location and routing for cold chain logistics networks with heterogeneous customer demand[J]. Journal of Industrial Information Integration, 2024, 38: 100573. doi: 10.1016/j.jii.2024.100573
    [37]
    HU Zi-qiang, WEI Yu-guang, AN Ran, et al. Optimization of routing and traffic allocation in multimodal transportation network with complex network structure and environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(3): 44-60.
    [38]
    ZHANG Xu, YUAN Xu-mei, JIANG Ya-di. Optimization of multimodal transportation under uncertain demand and stochastic carbon trading price[J]. Systems Engineering -Theory and Practice, 2021, 41(10): 2609-2620.
    [39]
    WANG Z J, ZHANG D Z, TAVASSZY L, et al. The hierarchical multimodal hub location problem for cross-border logistics networks considering multiple capacity levels, congestion and economies of scale[J]. Transportation Research Part E: Logistics and Transportation Review, 2025, 196: 103972. doi: 10.1016/j.tre.2025.103972
    [40]
    GAO T H, TIAN J, HUANG C, et al. The impact of new western land and sea corridor development on port deep hinterland transport service and route selection[J]. Ocean and Coastal Management, 2024, 247: 106910. doi: 10.1016/j.ocecoaman.2023.106910
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