IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i14p3244-d1201147.html
   My bibliography  Save this article

Optimum Route and Transport Mode Selection of Multimodal Transport with Time Window under Uncertain Conditions

Author

Listed:
  • Lin Li

    (School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China)

  • Qiangwei Zhang

    (School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China)

  • Tie Zhang

    (School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China)

  • Yanbiao Zou

    (School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China)

  • Xing Zhao

    (Kuayue Express, Shenzhen 223001, China)

Abstract

Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified by the fuzzy expectation value method and fuzzy chance-constrained planning method. Combining the game theory method with the weighted sum method, a cooperative game theory-based multi-objective optimization method is proposed. Finally, the effectiveness of the algorithm is verified in a real intermodal network. The experimental results show that the proposed method can effectively improve the performance of the weighted sum method and obtain the optimal multimodal transport path that satisfies the time window requirement, and the path optimization results are better than MOPSO and NSGA-II, effectively reducing transportation costs and carbon emissions. Meanwhile, the influence of uncertainty factors on the multimodal transport route planning results is analyzed. The results show that the uncertain factors will significantly increase the transportation cost and carbon emissions and affect the choice of route and transportation mode. Considering uncertainty factors can increase the reliability of route planning results and provide a more robust and effective solution for multimodal transportation.

Suggested Citation

  • Lin Li & Qiangwei Zhang & Tie Zhang & Yanbiao Zou & Xing Zhao, 2023. "Optimum Route and Transport Mode Selection of Multimodal Transport with Time Window under Uncertain Conditions," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3244-:d:1201147
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/14/3244/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/14/3244/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yi Zhang & Guowei Hua & T. C. E. Cheng & Juliang Zhang, 2020. "Cold chain distribution: How to deal with node and arc time windows?," Annals of Operations Research, Springer, vol. 291(1), pages 1127-1151, August.
    2. Resat, Hamdi G. & Turkay, Metin, 2015. "Design and operation of intermodal transportation network in the Marmara region of Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 16-33.
    3. Bing Han & Shanshan Shi & Haotian Gao & Yan Hu, 2022. "A Sustainable Intermodal Location-Routing Optimization Approach: A Case Study of the Bohai Rim Region," Sustainability, MDPI, vol. 14(7), pages 1-27, March.
    4. Adil Baykasoğlu & Kemal Subulan, 2019. "A fuzzy-stochastic optimization model for the intermodal fleet management problem of an international transportation company," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(8), pages 777-824, November.
    5. Yi Zhao & Ronghui Liu & Xi Zhang & Anthony Whiteing, 2018. "A chance-constrained stochastic approach to intermodal container routing problems," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-22, February.
    6. Zheng, Huan-yu & Wang, Ling, 2015. "Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm," International Journal of Production Economics, Elsevier, vol. 164(C), pages 421-432.
    7. Yue Lu & Maoxiang Lang & Xueqiao Yu & Shiqi Li, 2019. "A Sustainable Multimodal Transport System: The Two-Echelon Location-Routing Problem with Consolidation in the Euro–China Expressway," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    8. Demir, Emrah & Burgholzer, Wolfgang & Hrušovský, Martin & Arıkan, Emel & Jammernegg, Werner & Woensel, Tom Van, 2016. "A green intermodal service network design problem with travel time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 789-807.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Caiyi Wu & Yinggui Zhang & Yang Xiao & Weiwei Mo & Yuxie Xiao & Juan Wang, 2024. "Optimization of Multimodal Paths for Oversize and Heavyweight Cargo under Different Carbon Pricing Policies," Sustainability, MDPI, vol. 16(15), pages 1-23, August.
    2. Pei Zhu & Xiaolong Lv & Quan Shao & Caijin Kuang & Weiwang Chen, 2024. "Optimization of Green Multimodal Transport Schemes Considering Order Consolidation under Uncertainty Conditions," Sustainability, MDPI, vol. 16(15), pages 1-29, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Volha Yakavenka & Ioannis Mallidis & Dimitrios Vlachos & Eleftherios Iakovou & Zafeiriou Eleni, 2020. "Development of a multi-objective model for the design of sustainable supply chains: the case of perishable food products," Annals of Operations Research, Springer, vol. 294(1), pages 593-621, November.
    2. Chia-Nan Wang & Thanh-Tuan Dang & Tran Quynh Le & Panitan Kewcharoenwong, 2020. "Transportation Optimization Models for Intermodal Networks with Fuzzy Node Capacity, Detour Factor, and Vehicle Utilization Constraints," Mathematics, MDPI, vol. 8(12), pages 1-27, November.
    3. Yi Zhao & Qingwan Xue & Xi Zhang, 2018. "Stochastic Empty Container Repositioning Problem with CO 2 Emission Considerations for an Intermodal Transportation System," Sustainability, MDPI, vol. 10(11), pages 1-24, November.
    4. Thibault Delbart & Yves Molenbruch & Kris Braekers & An Caris, 2021. "Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions," Sustainability, MDPI, vol. 13(7), pages 1-25, April.
    5. Yan Sun & Xinya Li, 2019. "Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncerta," Mathematics, MDPI, vol. 7(8), pages 1-40, August.
    6. Adil Baykasoğlu & Nurhan Dudaklı & Kemal Subulan & A. Serdar Taşan, 2022. "An integrated fleet planning model with empty vehicle repositioning for an intermodal transportation system," Operational Research, Springer, vol. 22(3), pages 2063-2098, July.
    7. Archetti, Claudia & Peirano, Lorenzo & Speranza, M. Grazia, 2022. "Optimization in multimodal freight transportation problems: A Survey," European Journal of Operational Research, Elsevier, vol. 299(1), pages 1-20.
    8. Shou-feng Ji & Rong-juan Luo, 2017. "A Hybrid Estimation of Distribution Algorithm for Multi-Objective Multi-Sourcing Intermodal Transportation Network Design Problem Considering Carbon Emissions," Sustainability, MDPI, vol. 9(7), pages 1-24, June.
    9. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    10. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    11. Alexandra TUDORICA & Cristian Silviu BANACU, 2018. "A Review Of Public Measures For Supporting The Development Of Rail-Road Intermodal Freight Transport In Romania," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(1), pages 165-173, November.
    12. Yue Lu & Maoxiang Lang & Xueqiao Yu & Shiqi Li, 2019. "A Sustainable Multimodal Transport System: The Two-Echelon Location-Routing Problem with Consolidation in the Euro–China Expressway," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    13. Tareq Abu Aisha & Mustapha Ouhimmou & Marc Paquet, 2020. "Optimization of Container Terminal Layouts in the Seaport—Case of Port of Montreal," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
    14. Taiba Zahid & Fouzia Gillani & Usman Ghafoor & Muhammad Raheel Bhutta, 2022. "Synchromodal Transportation Analysis of the One-Belt-One-Road Initiative Based on a Bi-Objective Mathematical Model," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    15. Li, Zhaojin & Liu, Ya & Yang, Zhen, 2021. "An effective kernel search and dynamic programming hybrid heuristic for a multimodal transportation planning problem with order consolidation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    16. Liu, Chuanju & Lin, Shaochong & Shen, Zuo-Jun Max & Zhang, Junlong, 2023. "Stochastic service network design: The value of fixed routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    17. Zhao, Yiran & Yang, Zhongzhen & Haralambides, Hercules, 2019. "Optimizing the transport of export containers along China's coronary artery: The Yangtze River," Journal of Transport Geography, Elsevier, vol. 77(C), pages 11-25.
    18. Zhang, Yimeng & Li, Xinlei & van Hassel, Edwin & Negenborn, Rudy R. & Atasoy, Bilge, 2022. "Synchromodal transport planning considering heterogeneous and vague preferences of shippers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    19. Volodymyr Polishchuk & Miroslav Kelemen & Beáta Gavurová & Costas Varotsos & Rudolf Andoga & Martin Gera & John Christodoulakis & Radovan Soušek & Jaroslaw Kozuba & Peter Blišťan & Stanislav Szabo, 2019. "A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    20. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3244-:d:1201147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.