IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v358y2024ics0306261924000400.html
   My bibliography  Save this article

LNG market liberalization and LNG transportation: Evaluation based on fleet size and composition model

Author

Listed:
  • Yuan, Jun
  • Shi, Xunpeng
  • He, Junliang

Abstract

The proportion of spot trading and short-term contracts has gradually increased in the rapidly growing LNG market, leading to more uncertainties in LNG demand and prices that significantly challenge LNG shipping decisions. In this paper, a mathematical model is developed to minimize transportation costs from multiple exporting countries to multiple importing countries under demand and price uncertainty, both of which are results of LNG market liberalization. A Gaussian process metamodel based simulation optimization method is proposed to solve the fleet planning problem, accounting for various uncertainties. A case study is given to illustrate the effects of uncertainties on the optimal solutions. The results demonstrate that shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. LNG market liberalization can significantly reduce its transportation costs and carbon emissions. The results suggest the need for further LNG market liberalization and measures to mitigate uncertainties for shipping companies, such as removing destination clauses.

Suggested Citation

  • Yuan, Jun & Shi, Xunpeng & He, Junliang, 2024. "LNG market liberalization and LNG transportation: Evaluation based on fleet size and composition model," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261924000400
    DOI: 10.1016/j.apenergy.2024.122657
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924000400
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.122657?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Babai, M. Zied & Ivanov, Dmitry & Kwon, Oh Kang, 2023. "Optimal ordering quantity under stochastic time-dependent price and demand with a supply disruption: A solution based on the change of measure technique," Omega, Elsevier, vol. 116(C).
    2. Arslan, Ayşe N. & Papageorgiou, Dimitri J., 2017. "Bulk ship fleet renewal and deployment under uncertainty: A multi-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 69-96.
    3. Jokinen, Raine & Pettersson, Frank & Saxén, Henrik, 2015. "An MILP model for optimization of a small-scale LNG supply chain along a coastline," Applied Energy, Elsevier, vol. 138(C), pages 423-431.
    4. Awudu, Iddrisu & Zhang, Jun, 2013. "Stochastic production planning for a biofuel supply chain under demand and price uncertainties," Applied Energy, Elsevier, vol. 103(C), pages 189-196.
    5. Haowei Wang & Jun Yuan & Szu Hui Ng, 2020. "Gaussian process based optimization algorithms with input uncertainty," IISE Transactions, Taylor & Francis Journals, vol. 52(4), pages 377-393, April.
    6. Schulte, Benedikt & Sachs, Anna-Lena, 2020. "The price-setting newsvendor with Poisson demand," European Journal of Operational Research, Elsevier, vol. 283(1), pages 125-137.
    7. Jang, Hayoung & Jeong, Byongug & Zhou, Peilin & Ha, Seungman & Nam, Dong, 2021. "Demystifying the lifecycle environmental benefits and harms of LNG as marine fuel," Applied Energy, Elsevier, vol. 292(C).
    8. Liu, Shuguang & Huang, Weilai & Ma, Huiming, 2009. "An effective genetic algorithm for the fleet size and mix vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(3), pages 434-445, May.
    9. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    10. Kristin Uggen & Marte Fodstad & Vibeke Nørstebø, 2013. "Using and extending fix-and-relax to solve maritime inventory routing problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 355-377, July.
    11. Shi, Xunpeng & Padinjare Variam, Hari Malamakkavu, 2016. "Gas and LNG trading hubs, hub indexation and destination flexibility in East Asia," Energy Policy, Elsevier, vol. 96(C), pages 587-596.
    12. Koza, David Franz & Ropke, Stefan & Boleda Molas, Anna, 2017. "The liquefied natural gas infrastructure and tanker fleet sizing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 96-114.
    13. Sun, Lijun & Zhang, Yuankai & Hu, Xiangpei, 2021. "Economical-traveling-distance-based fleet composition with fuel costs: An application in petrol distribution," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    14. Qiang Meng & Tingsong Wang, 2010. "A chance constrained programming model for short-term liner ship fleet planning problems," Maritime Policy & Management, Taylor & Francis Journals, vol. 37(4), pages 329-346, July.
    15. Salhi, Said & Wassan, Niaz & Hajarat, Mutaz, 2013. "The Fleet Size and Mix Vehicle Routing Problem with Backhauls: Formulation and Set Partitioning-based Heuristics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 22-35.
    16. Postali, Fernando A.S. & Picchetti, Paulo, 2006. "Geometric Brownian Motion and structural breaks in oil prices: A quantitative analysis," Energy Economics, Elsevier, vol. 28(4), pages 506-522, July.
    17. Shi, Xunpeng & Shen, Yifan, 2021. "Macroeconomic uncertainty and natural gas prices: Revisiting the Asian Premium," Energy Economics, Elsevier, vol. 94(C).
    18. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    19. Pantuso, Giovanni & Fagerholt, Kjetil & Hvattum, Lars Magnus, 2014. "A survey on maritime fleet size and mix problems," European Journal of Operational Research, Elsevier, vol. 235(2), pages 341-349.
    20. Olmez Turan, Merve & Flamand, Tulay, 2023. "Optimizing investment and transportation decisions for the European natural gas supply chain," Applied Energy, Elsevier, vol. 337(C).
    21. Meng, Qiang & Wang, Tingsong & Wang, Shuaian, 2012. "Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand," European Journal of Operational Research, Elsevier, vol. 223(1), pages 96-105.
    22. Wang, Xin & Fagerholt, Kjetil & Wallace, Stein W., 2018. "Planning for charters: A stochastic maritime fleet composition and deployment problem," Omega, Elsevier, vol. 79(C), pages 54-66.
    23. Nian, Victor & Yuan, Jun, 2017. "A method for analysis of maritime transportation systems in the life cycle approach – The oil tanker example," Applied Energy, Elsevier, vol. 206(C), pages 1579-1589.
    Full references (including those not matched with items on IDEAS)

    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. Ksciuk, Jana & Kuhlemann, Stefan & Tierney, Kevin & Koberstein, Achim, 2023. "Uncertainty in maritime ship routing and scheduling: A Literature review," European Journal of Operational Research, Elsevier, vol. 308(2), pages 499-524.
    2. Ng, ManWo & Lin, Dung-Ying, 2018. "Fleet deployment in liner shipping with incomplete demand information," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 184-189.
    3. Ng, ManWo, 2017. "Revisiting a class of liner fleet deployment models," European Journal of Operational Research, Elsevier, vol. 257(3), pages 773-776.
    4. Stålhane, Magnus & Halvorsen-Weare, Elin E. & Nonås, Lars Magne & Pantuso, Giovanni, 2019. "Optimizing vessel fleet size and mix to support maintenance operations at offshore wind farms," European Journal of Operational Research, Elsevier, vol. 276(2), pages 495-509.
    5. Ng, ManWo, 2014. "Distribution-free vessel deployment for liner shipping," European Journal of Operational Research, Elsevier, vol. 238(3), pages 858-862.
    6. Wu, Lingxiao & Wang, Shuaian & Laporte, Gilbert, 2021. "The Robust Bulk Ship Routing Problem with Batched Cargo Selection," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 124-159.
    7. Arslan, Ayşe N. & Papageorgiou, Dimitri J., 2017. "Bulk ship fleet renewal and deployment under uncertainty: A multi-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 69-96.
    8. Orestis Schinas & Niklas Bergmann, 2021. "The Short-Term Cost of Greening the Global Fleet," Sustainability, MDPI, vol. 13(16), pages 1-32, August.
    9. Koza, David Franz & Ropke, Stefan & Boleda Molas, Anna, 2017. "The liquefied natural gas infrastructure and tanker fleet sizing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 96-114.
    10. Yang, Zhongzhen & Jiang, Zhenfeng & Notteboom, Theo & Haralambides, Hercules, 2019. "The impact of ship scrapping subsidies on fleet renewal decisions in dry bulk shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 177-189.
    11. Wang, Xin & Fagerholt, Kjetil & Wallace, Stein W., 2018. "Planning for charters: A stochastic maritime fleet composition and deployment problem," Omega, Elsevier, vol. 79(C), pages 54-66.
    12. Zhao, Yue & Chen, Zhi & Lim, Andrew & Zhang, Zhenzhen, 2022. "Vessel deployment with limited information: Distributionally robust chance constrained models," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 197-217.
    13. Zhang, Lingge & Yang, Dong & Wu, Shining & Luo, Meifeng, 2023. "Revisiting the pricing benchmarks for Asian LNG — An equilibrium analysis," Energy, Elsevier, vol. 262(PA).
    14. Wang, Tiantian & Qu, Wan & Zhang, Dayong & Ji, Qiang & Wu, Fei, 2022. "Time-varying determinants of China's liquefied natural gas import price: A dynamic model averaging approach," Energy, Elsevier, vol. 259(C).
    15. Qiang Meng & Shuaian Wang & Henrik Andersson & Kristian Thun, 2014. "Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions," Transportation Science, INFORMS, vol. 48(2), pages 265-280, May.
    16. E. Zhang & Feng Chu & Shijin Wang & Ming Liu & Yang Sui, 2022. "Approximation approach for robust vessel fleet deployment problem with ambiguous demands," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2180-2194, November.
    17. Bittante, A. & Pettersson, F. & Saxén, H., 2018. "Optimization of a small-scale LNG supply chain," Energy, Elsevier, vol. 148(C), pages 79-89.
    18. Vidal, Thibaut & Laporte, Gilbert & Matl, Piotr, 2020. "A concise guide to existing and emerging vehicle routing problem variants," European Journal of Operational Research, Elsevier, vol. 286(2), pages 401-416.
    19. Zhen, Lu & Hu, Yi & Wang, Shuaian & Laporte, Gilbert & Wu, Yiwei, 2019. "Fleet deployment and demand fulfillment for container shipping liners," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 15-32.
    20. Lai, Xiaofan & Wu, Lingxiao & Wang, Kai & Wang, Fan, 2022. "Robust ship fleet deployment with shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 169-196.

    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:eee:appene:v:358:y:2024:i:c:s0306261924000400. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.