Robust optimization model of container liner routes in feeder line network
Abstract
The universal application of the hub-and-spoke maritime network makes feeder line network key to restricting the quality and efficiency of maritime transportation. However, container liner routes in feeder line network are susceptible to the changes in shipment demand and international fuel prices. Therefore, based on the hub-and-spoke maritime network, this paper constructs a robust optimization model of container liner routes in feeder line network. Under the capacity and time constraints, routes optimization and ship equipment under uncertain environment are analysed. An improved tabu search algorithm was designed based on the characteristics of the model. The example analysis proves that the model can still ensure the robustness of routes under uncertain environment, which is more applicable than the deterministic model.
Keyword : container liner routes, hub-and-spoke, feeder line, uncertain environment, robust optimization model, improved tabu search
How to Cite
Huang, X., Chen, H., Zhang, J., Wang, D., Chen, J., & Luo, J. X. (2024). Robust optimization model of container liner routes in feeder line network. Transport, 39(1), 13–24. https://doi.org/10.3846/transport.2024.20531
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Tuljak-Suban, D. 2018. Competition or cooperation in a hub and spoke-shipping network: the case of the North Adriatic container terminals, Transport 33(2): 429–436. https://doi.org/10.3846/16484142.2016.1261368
Wang, S.; Alharbi, A.; Davy, P. 2014. Liner ship route schedule design with port time windows, Transportation Research Part C: Emerging Technologies 41: 1–17. https://doi.org/10.1016/j.trc.2014.01.012
Wang, S.; Meng, Q. 2012. Robust schedule design for liner shipping services, Transportation Research Part E: Logistics and Transportation Review 48(6): 1093–1106. https://doi.org/10.1016/j.tre.2012.04.007
Wang, T.; Meng, Q.; Wang, S. 2012. Robust optimization model for liner ship fleet planning with container transshipment and uncertain demand, Transportation Research Record: Journal of the Transportation Research Board 2273: 18–28. https://doi.org/10.3141/2273-03
Wang, X.; Teo, C.-C. 2013. Integrated hedging and network planning for container shipping’s bunker fuel management, Maritime Economics & Logistics 15(2): 172–196. https://doi.org/10.1057/mel.2013.5
Wind. 2018. Bohai Sea Port Group Freight Rate. Wind Information Co. Ltd., Shanghai, China. Available from Internet: https://www.wind.com.cn
Xing, Y.; Yang, H.; Ma, X. 2018. Optimization of containership sailing speed and fleet deployment for continental ocean liner based on freight rate differentiation strategy, Systems Engineering – Theory & Practice 38(12): 3222–3234. https://doi.org/10.12011/1000-6788(2018)12-3222-13 (in Chinese).
Xing, Y.; Yang, H.; Ma, X.; Zhang, Y. 2019. Optimization of ship speed and fleet deployment under carbon emissions policies for container shipping, Transport 34(2): 260–274. https://doi.org/10.3846/transport.2019.9317
Xing, Y.; Yang, H.; Zhang, Y. 2017. Optimization model and algorithm of containership speed and fleet planning on ocean route basis, Navigation of China (2): 119–124. (in Chinese).
Xu, C.; Hu, J.; Huang, Y. 2020. Robust optimization of express delivery network with uncertain demand, Computer Engineering and Applications (3): 272–278. Available from Internet: http://kns.cnki.net/kcms/detail/11.2127.TP.20190327.1829.020.html (in Chinese).
Xu, W. 2011. Research on Bunker Price Volatility and Hedging Strategy. MSc Thesis. Dalian Maritime University, China. Available from Internet: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFD2011&filename=1011111025.nh
Yang, H.; Zhao, X.; Liu, B.; Feng, Q. 2017. Robust optimization of container feeder logistics network under uncertain demand, Logistics Technology 36(2): 90–94. https://doi.org/10.3969/j.issn.1005-152X.2017.02.022 (in Chinese).
Yang, L.-Q. 2015. Scheduling model of feeder line multi-hull container ships based on hub-and-spoke network, Chinese Journal of Management Science (S1): 860–864. (in Chinese).
Yao, Z.; Ng, S. H.; Lee, L. H. 2012. A study on bunker fuel management for the shipping liner services, Computers & Operations Research 39(5): 1160–1172. https://doi.org/10.1016/j.cor.2011.07.012
Zhang, J.-H.; Li, J.; Gao, M.-G. 2013. Research on the open vehicle routing problem of with simultaneous deliveries and pickups, Chinese Journal of Management Science (4): 187–192. https://doi.org/10.16381/j.cnki.issn1003-207x.2013.04.010 (in Chinese).
Zhang, S.; Chen, M.; Zhang, W. 2019. A novel location-routing problem in electric vehicle transportation with stochastic demands, Journal of Cleaner Production 221: 567–581. https://doi.org/10.1016/j.jclepro.2019.02.167
Zhao, X.; Cao, B.-M.; Dou, J.-P. 2017. Robust optimal design of agri-food supply chain network under demand uncertainty and raw material price uncertainty, Journal of Industrial Engineering and Engineering Management (4): 178–185. https://doi.org/10.13587/j.cnki.jieem.2017.04.023 (in Chinese).
Zheng, J.; Yang, D. 2016. Hub-and-spoke network design for container shipping along the Yangtze river, Journal of Transport Geography 55: 51–57. https://doi.org/10.1016/j.jtrangeo.2016.07.001
Du, J.; Zhao, X.; Ji, M. J. 2017. Inner branch container liner route network planning model considering cargo owners’ preferences, Journal of Transportation Engineering 17(3): 131–140. https://doi.org/10.3969/j.issn.1671-1637.2017.03.014 (in Chinese).
Du, G.; Sun, C.; Weng, J. 2016. Liner shipping fleet deployment with sustainable collaborative transportation, Sustainability 8(2): 165. https://doi.org/10.3390/su8020165
Fan, H.; Li, C.; Jiang, X.; Xu, Z. 2018. The location of inland ports considering the reliability of the route under uncertain demand, Journal of Management 15(8): 1256–1264. https://doi.org/10.3969/j.issn.1672-884x.2018.08.019 (in Chinese).
Jung, H.; Jeong, S.-J. 2012. Managing demand uncertainty through fuzzy inference in supply chain planning, International Journal of Production Research 50(19): 5415–5429. https://doi.org/10.1080/00207543.2011.631606
Kepaptsoglou, K.; Fountas, G.; Karlaftis, M. G. 2015. Weather impact on containership routing in closed seas: a chance-constraint optimization approach, Transportation Research Part C: Emerging Technologies 55: 139–155. https://doi.org/10.1016/j.trc.2015.01.027
Kong, Y. 2015. Research on Fuzzy Resource Constrained Project Scheduling Robust Optimization Based on Possibility Theory. MSc Thesis. China University of Petroleum. Available from Internet: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFD201801&filename=1017809738.nh (in Chinese).
Li, J.-Y.; He, Z.-W. 2015. Optimization of reactive multi-mode project scheduling based on stochastic breakdown of resources, Operations Research and Management Science 24(6): 44–50. Available from Internet: http://www.jorms.net/EN/10.12005/orms.2015.0194 (in Chinese).
Li, S.; Liu, R.; Yang, L.; Gao, Z. 2019. Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty, Transportation Research Part B: Methodological 123: 88–109. https://doi.org/10.1016/j.trb.2019.03.019
Li, X.; Zhong, Z.; Zhao, Q.; Yuan, R. 2017. Robust optimization for project portfolio selection problem with divisibility under information uncertainty, Systems Engineering – Theory & Practice 37(11): 2908–2917. https://doi.org/10.12011/1000-6788(2017)11-2908-10 (in Chinese).
Li, Y.; Fan, H.; Zhang, X.; Yang, X. 2018. Two-phase variable neighborhood tabu search for the capacitated vehicle routing problem with fuzzy demand, Systems Engineering – Theory & Practice 38(2): 522–531. https://doi.org/10.12011/1000-6788(2018)02-0522-10 (in Chinese).
Liu, H.; Yang, C. 2016. Robust research on service facility network design problem with demand uncertainty, Operations Research and Management Science 25(1): 117–125. Available from Internet: http://www.jorms.net/EN/10.12005/orms.2016.0016 (in Chinese).
Magirou, E. F.; Psaraftis, H. N.; Bouritas, T. 2015. The economic speed of an oceangoing vessel in a dynamic setting, Transportation Research Part B: Methodological 76: 48–67. https://doi.org/10.1016/j.trb.2015.03.001
Meng, Q.; Wang, T. 2011. A scenario-based dynamic programming model for multi-period liner ship fleet planning, Transportation Research Part E: Logistics and Transportation Review 47(4): 401–413. https://doi.org/10.1016/j.tre.2010.12.005
Mole, R. H.; Jameson, S. R. 1976. A sequential route-building algorithm employing a generalised savings criterion, Journal of the Operational Research Society 27(2): 503–511. https://doi.org/10.1057/jors.1976.95
Ronen, D. 2011. The effect of oil price on containership speed and fleet size, Journal of the Operational Research Society 62(1): 211–216. https://doi.org/10.1057/jors.2009.169
Shintani, K.; Imai, A.; Nishimura, E.; Papadimitriou, S. 2007. The container shipping network design problem with empty container repositioning, Transportation Research Part E: Logistics and Transportation Review 43(1): 39–59. https://doi.org/10.1016/j.tre.2005.05.003
Song, D.-P.; Li, D.; Drake, P. 2015. Multi-objective optimization for planning liner shipping service with uncertain port times, Transportation Research Part E: Logistics and Transportation Review 84: 1–22. https://doi.org/10.1016/j.tre.2015.10.001
Sun, H.-L.; Cui, Q.-Y.; Xue, Y.-F. 2017. Robust optimization for location-routing problem with uncertain demand under risk, Operations Research and Management Science 26(11): 26–34. Available from Internet: http://www.jorms.net/EN/10.12005/orms.2017.0256 (in Chinese).
Tran, N. K.; Haasis, H.-D. 2018. A research on operational patterns in container liner shipping, Transport 33(3): 619–632. https://doi.org/10.3846/transport.2018.1571
Tuljak-Suban, D. 2018. Competition or cooperation in a hub and spoke-shipping network: the case of the North Adriatic container terminals, Transport 33(2): 429–436. https://doi.org/10.3846/16484142.2016.1261368
Wang, S.; Alharbi, A.; Davy, P. 2014. Liner ship route schedule design with port time windows, Transportation Research Part C: Emerging Technologies 41: 1–17. https://doi.org/10.1016/j.trc.2014.01.012
Wang, S.; Meng, Q. 2012. Robust schedule design for liner shipping services, Transportation Research Part E: Logistics and Transportation Review 48(6): 1093–1106. https://doi.org/10.1016/j.tre.2012.04.007
Wang, T.; Meng, Q.; Wang, S. 2012. Robust optimization model for liner ship fleet planning with container transshipment and uncertain demand, Transportation Research Record: Journal of the Transportation Research Board 2273: 18–28. https://doi.org/10.3141/2273-03
Wang, X.; Teo, C.-C. 2013. Integrated hedging and network planning for container shipping’s bunker fuel management, Maritime Economics & Logistics 15(2): 172–196. https://doi.org/10.1057/mel.2013.5
Wind. 2018. Bohai Sea Port Group Freight Rate. Wind Information Co. Ltd., Shanghai, China. Available from Internet: https://www.wind.com.cn
Xing, Y.; Yang, H.; Ma, X. 2018. Optimization of containership sailing speed and fleet deployment for continental ocean liner based on freight rate differentiation strategy, Systems Engineering – Theory & Practice 38(12): 3222–3234. https://doi.org/10.12011/1000-6788(2018)12-3222-13 (in Chinese).
Xing, Y.; Yang, H.; Ma, X.; Zhang, Y. 2019. Optimization of ship speed and fleet deployment under carbon emissions policies for container shipping, Transport 34(2): 260–274. https://doi.org/10.3846/transport.2019.9317
Xing, Y.; Yang, H.; Zhang, Y. 2017. Optimization model and algorithm of containership speed and fleet planning on ocean route basis, Navigation of China (2): 119–124. (in Chinese).
Xu, C.; Hu, J.; Huang, Y. 2020. Robust optimization of express delivery network with uncertain demand, Computer Engineering and Applications (3): 272–278. Available from Internet: http://kns.cnki.net/kcms/detail/11.2127.TP.20190327.1829.020.html (in Chinese).
Xu, W. 2011. Research on Bunker Price Volatility and Hedging Strategy. MSc Thesis. Dalian Maritime University, China. Available from Internet: https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFD2011&filename=1011111025.nh
Yang, H.; Zhao, X.; Liu, B.; Feng, Q. 2017. Robust optimization of container feeder logistics network under uncertain demand, Logistics Technology 36(2): 90–94. https://doi.org/10.3969/j.issn.1005-152X.2017.02.022 (in Chinese).
Yang, L.-Q. 2015. Scheduling model of feeder line multi-hull container ships based on hub-and-spoke network, Chinese Journal of Management Science (S1): 860–864. (in Chinese).
Yao, Z.; Ng, S. H.; Lee, L. H. 2012. A study on bunker fuel management for the shipping liner services, Computers & Operations Research 39(5): 1160–1172. https://doi.org/10.1016/j.cor.2011.07.012
Zhang, J.-H.; Li, J.; Gao, M.-G. 2013. Research on the open vehicle routing problem of with simultaneous deliveries and pickups, Chinese Journal of Management Science (4): 187–192. https://doi.org/10.16381/j.cnki.issn1003-207x.2013.04.010 (in Chinese).
Zhang, S.; Chen, M.; Zhang, W. 2019. A novel location-routing problem in electric vehicle transportation with stochastic demands, Journal of Cleaner Production 221: 567–581. https://doi.org/10.1016/j.jclepro.2019.02.167
Zhao, X.; Cao, B.-M.; Dou, J.-P. 2017. Robust optimal design of agri-food supply chain network under demand uncertainty and raw material price uncertainty, Journal of Industrial Engineering and Engineering Management (4): 178–185. https://doi.org/10.13587/j.cnki.jieem.2017.04.023 (in Chinese).
Zheng, J.; Yang, D. 2016. Hub-and-spoke network design for container shipping along the Yangtze river, Journal of Transport Geography 55: 51–57. https://doi.org/10.1016/j.jtrangeo.2016.07.001