To establish the cross-border logistics hub network for China's Belt and Road Initiative, this study proposes a multi-objective hub location model that integrates two novel metrics: hub network utilization rate and hub economic stimulating effect. These metrics are formulated to evaluate resource efficiency and regional economic growth potential, respectively, and are incorporated into the optimization framework. The model is solved using the non-dominated sorting genetic algorithm III (NSGA-III) with a training population, yielding a Pareto-optimal solution of 44 international logistics hubs across the Belt and Road regions, including Asia, Europe, West Africa, Oceania, and Central America. Results demonstrate that prioritizing these two objectives significantly enhances their performance: every time doubling the cost, the hub network utilization rate increases to 1.71 times and the hub economic stimulating effect rises to 1.60 times the baseline. This highlights the model's theoretical and practical value in improving resource efficiency and fostering sustainable economic development for the Belt and Road countries at lower costs. Key management insights include advancing cross-border railroads linking China and the Middle East to integrate the China Railway Express with East China, incorporating strategic ports in Oceania and North Africa into the “21st-Century Maritime Silk Road”, enhancing seaport hubs in South China and Southern Europe, and exploring direct routes between western China and South America. These measures aim to strengthen connectivity, optimize hub utilization, and amplify economic synergies across the Belt and Road network.
CuiL.SongM.Economic Evaluation of the Belt and Road Initiative from an Unimpeded Trade Perspective. International Journal of Logistics Research and Applications, Vol. 22, No. 1, 2019, pp. 25–46.
2.
ZhangD.ZhangF.LiangY.An Evolutionary Model of the International Logistics Network Based on the Belt and Road Perspective. Physica A: Statistical Mechanics and its Applications, Vol. 572, 2021, p. 125867.
3.
LuH.LiL.ZhaoX.CookD.A Model of Integrated Regional Logistics Hub in Supply Chain. Enterprise Information Systems, Vol. 12, No. 10, 2018, pp. 1308–1335.
4.
National Information Center “Belt and Road” Big Data Center, Dalian Hanwen Information Co., Ltd. (2018) The Belt and Road Trade Cooperation Big Data Report 2018. Belt and Road Portal, China, May.
5.
YanC.WangX.YangK.Fuzzy Reliability Optimization for 2-Hub Center Problem with Cluster-Based Policy and Application in Cross-Border Supply Chain Network Design Using TS Algorithm. Complexity, Vol. 2020, 2020, p. 5065640.
6.
ZhangX.LiuC.PengY.Accessibility-Based Location of International Multimodal Logistics Hubs: A Case in China. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 564–585. https://doi.org/10.1177/03611981221096440
7.
ZhangX.LuJ.PengY.Decision Framework for Location and Selection of Container Multimodal Hubs: A Case in China Under the Belt and Road Initiative. Journal of Industrial and Management Optimization, Vol. 18, No. 3, 2022, p. 2163.
8.
WeiH. R.Optimal Design of an Integrated Cross-Border Logistics Network for China’s Inland Regions. Journal of Coastal Research, Vol. 37, No. 3, 2021, pp. 644–655.
9.
O’KellyM. E.A Quadratic Integer Program for the Location of Interacting Hub Facilities. European Journal of Operational Research, Vol. 32, No. 3, 1987, pp. 393–404.
10.
MokhtarH.RediA. P.KrishnamoorthyM.ErnstA. T.An Intermodal Hub Location Problem for Container Distribution in Indonesia. Computers & Operations Research, Vol. 104, 2019, pp. 415–432.
11.
XuW.HuangJ.QiuY.Study on the Optimization of Hub-and-Spoke Logistics Network Regarding Traffic Congestion. Journal of Advanced Transportation, Vol. 2021, 2021, p. 8711964.
12.
WangY.HuangY.Incremental Optimization of Hub and Spoke Network for the Spokes’ Numbers and Flow. Mathematical Problems in Engineering, Vol. 2015, 2015, p. 682402.
13.
HaiF.YanX.DingC.ShaoX.Nodes Selection of Regional Logistics Network Based on Hub-and-Spoke Theory. Computer Integrated Manufacturing Systems, Vol. 18, No. 6, 2012, pp. 1299–1305.
14.
Ghaffari–NasabN.GhazanfariM.SabouryA.FathollahM.The Single Allocation Hub Location Problem: A Robust Optimisation Approach. European Journal of Industrial Engineering, Vol. 9, No. 2, 2015, pp. 147–170.
15.
AmbrosinoD.SciomachenA.A Capacitated Hub Location Problem in Freight Logistics Multimodal Networks. Optimization Letters, Vol. 10, No. 5, 2016, pp. 875–901.
16.
YangY.ZhangJ.SunW.PuY.Research on NSGA-III in Location-Routing-Inventory Problem of Pharmaceutical Logistics Intermodal Network. Journal of Intelligent & Fuzzy Systems, Vol. 41, No. 1, 2021, pp. 699–713.
17.
CreazzaA.DallariF.RossiT.An Integrated Model for Designing and Optimising an International Logistics Network. International Journal of Production Research, Vol. 50, No. 11, 2012, pp. 2925–2939.
18.
ChangZ.NotteboomT.LuJ.A Two-Phase Model for Dry Port Location with an Application to the Port of Dalian in China. Transportation Planning and Technology, Vol. 38, No. 4, 2015, pp. 442–464.
19.
WeiH.DongM.Import-Export Freight Organization and Optimization in the Dry-Port-Based Cross-Border Logistics Network Under the Belt and Road Initiative. Computers & Industrial Engineering, Vol. 130, 2019, pp. 472–484.
20.
ZhangZ.An Optimization Model for Logistics Distribution Network of Cross-Border E-Commerce Based on Personalized Recommendation Algorithm. Security and Communication Networks, Vol. 2021, 2021, p. 5510481.
21.
XieF. J.FengR. C.ZhouX. Y.Research on the Optimization of Cross-Border Logistics Paths of the Belt and Road in the Inland Regions. Journal of Advanced Transportation, Vol. 2022, 2022, p. 5776334.
22.
TsaoY. C.ThanhV. V.A Multi-Objective Mixed Robust Possibilistic Flexible Programming Approach for Sustainable Seaport-Dry Port Network Design Under an Uncertain Environment. Transportation Research Part E: Logistics and Transportation Review, Vol. 124, 2019, pp. 13–39.
23.
LeungS.WuY.LaiK.A Robust Optimization Model for a Cross-Border Logistics Problem with Fleet Composition in an Uncertain Environment. Mathematical and Computer Modelling, Vol. 36, No. 11–13, 2002, pp. 1221–1234.
24.
LeeD. H.DongM.BianW.The Design of Sustainable Logistics Network Under Uncertainty. International Journal of Production Economics, Vol. 128, No. 1, 2010, pp. 159–166.
25.
PanH. H.ZhangZ. C.LiuC. H.A Review of Logistics Hub Layout in China. Logistics Engineering and Management, Vol. 44, No. 4, 2022, pp. 1–4.
26.
LiuY. Y.Research on the Impact of National Logistics Hub Project Construction on the Efficiency of Urban Commerce and Trade Circulation. Commercial & Economic Research, Vol. 18, 2024, pp. 25–28.
27.
LouN.LuoL. Y.Empirical Study on the Impact of Cross-Border E-Commerce on China’s Import and Export Trade Based on Belt and Road. International Trade, Vol. 20, 2018, pp. 132–134.
28.
LiH. F.HuD. W.ChenX. Q.WangY.Expanding Hub Location-Routing Problem for Hybrid Hub-and-Spoke Multimodal Transport Network Considering Carbon Emissions. Journal of Traffic and Transportation Engineering, Vol. 22, No. 4, 2022, pp. 306–321.
29.
BenjaafarS.LiY.DaskinM. S.Carbon Footprint and the Management of Supply Chains: Insights From Simple Models. IEEE Transactions on Automation Science and Engineering: A Publication of the IEEE Robotics and Automation Society, Vol. 10, No. 1, 2013, pp. 99–116.
30.
FahimniaB.SarkisJ.ChoudharyA.EshraghA.Tactical Supply Chain Planning Under a Carbon Tax Policy Scheme: A Case Study. International Journal of Production Economics, Vol. 164, 2015, pp. 206–215.
31.
XieJ.LinG. L.HeH. D.ZhouH. L.Optimization of Multimodal Transportation in Fuzzy Demand Condition with Consideration of Carbon Cost. Journal of Ningxia University(Natural Science Edition), Vol. 38, No. 2, 2017, pp. 173–179.
32.
YuS. P.LiuJ.Optimization of Multimodal Transport Path Considering Carbon Emission. Energy Conservation & Environmental Protection in Transportation, Vol. 14, No. 6, 2018, pp. 38–41+78.
33.
GuM. X.Research on Optimization of Multimodal Transport Path Considering Carbon Emissions in Uncertain Environment. Jilin University, Changchun, China, 2020.
34.
QiY.HarrodS.PsaraftisH. N.LangM.Transport Service Selection and Routing with Carbon Emissions and Inventory Costs Consideration in the Context of the Belt and Road Initiative. Transportation Research Part E: Logistics and Transportation Review, Vol. 159, 2022, p. 102630.
35.
LiY. M.GuoX. Y.YangL.Route Optimization of China-EU Container Multimodal Transport Considering Various Factors. Journal of Railway Science and Engineering, Vol. 14, No. 10, 2017, pp. 2239–2248.
36.
YiC. Z.QiuH. Y.KeY. D.LiuM.WuZ. K.Collaborative Optimization for Multimodal Transport Network of Regional Main Hub Ports. Journal of Railway Science and Engineering, Vol. 19, No. 1, 2022, pp. 63–70.
37.
XuW.Adhering to the People-Centered Promotion of the Modernization of the Governance System and Governance Capacity. Theoretical Horizon, Vol. 241, No. 3, 2020, pp. 5–11.
38.
Anonymous. World Bank Group: Governance Matters 2009: Release of Worldwide Governance Indicators 1996–2008. M2 Presswire, 2009.
39.
MousazadeenM.TorabiS. A.PishvaeeM. S.Green and Reverse Logistics Management Under Fuzziness. In Supply Chain Management Under Fuzziness (KahramanC.ÖztayşiB., eds.), Springer, Berlin, Heidelberg, 2014, pp. 607–637.
40.
InuiguchiM.RamikJ.Possibilistic Linear Programming: A Brief Review of Fuzzy Mathematical Programming and a Comparison with Stochastic Programming in Portfolio Selection Problem. Fuzzy Sets and Systems, Vol. 111, No. 1, 2000, pp. 3–28.
41.
MulaJ.PolerR.GarciaJ.MRP with Flexible Constraints: A Fuzzy Mathematical Programming Approach. Fuzzy Sets and Systems, Vol. 157, No. 1, 2006, pp. 74–97.
42.
TorabiS. A.HassiniE.An Interactive Possibilistic Programming Approach for Multiple Objective Supply Chain Master Planning. Fuzzy Sets and Systems, Vol. 159, No. 2, 2008, pp. 193–214.
43.
PishvaeeM. S.RazmiJ.TorabiS. A.Robust Possibilistic Programming for Socially Responsible Supply Chain Network Design: A New Approach. Fuzzy Sets and Systems, Vol. 206, 2012, pp. 1–20.
DebK.AgrawalR. B.Simulated Binary Crossover for Continuous Search Space. Complex Systems, Vol. 9, No. 2, 1995, pp. 115–148.
46.
CensorY.Pareto Optimality in Multiobjective Problems. Applied Mathematics and Optimization, Vol. 4, No. 1, 1977, pp. 41–59.
47.
DebK.PratapA.AgarwalS.MeyarivanT.A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, 2002, pp. 182–197.
48.
DebK.GoyalM.A Combined Genetic Adaptive Search (GeneAS) for Engineering Design. Computer Science and Informatics, Vol. 26, 1996, pp. 30–45.
49.
Chinese Academy of International Trade and Economic Cooperation, Ministry of Commerce. The Belt and Road Trade and Investment Development Report 2021. Beijing, China, 2021.
50.
WangJ. H.MaS. Q.LiM.Construction of Free Trade Zone Networks Based on Central Nodes along the Belt and Road. China Business and Market, Vol. 35, No. 7, 2021, pp. 64–74.
