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Small-scale LNG Market Optimization – Intelligent Distribution Network

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020

Author

Listed:
  • Kuk, Edyta
  • Małkus, Bartłomiej
  • Kuk, Michał

Abstract

Intelligent Systems, thanks to their effectiveness and robustness, find many applications in various industries. One of such applications is optimization of distribution network of small-scale LNG market, which was highly dynamic throughout last years. LNG (Liquified Natural Gas) is a fuel produced from natural gas, but its volume is approx. 600 times smaller than in the gas (natural) state, which makes it more economically effective to transport and store. Distribution network consists of several pickup points (varying in LNG specification) and a number of destination points (varying in tanks capacities). From economic point of view, optimization of LNG truck tanks paths is an important factor in whole market development. The optimization process involves selecting a pickup point and a sequence of destination points with amount of LNG unloaded in each of them. Solution proposed in this paper is based on graph theory and advanced machine learning methods, such as reinforcement learning, recurrent neural networks and online learning. Optimization of distribution network translates directly into a number of economic benefits: reduction of LNG transport cost, shortening the delivery time, reduction of distribution costs and increase in the effectiveness of tank truck usage.

Suggested Citation

  • Kuk, Edyta & Małkus, Bartłomiej & Kuk, Michał, 2020. "Small-scale LNG Market Optimization – Intelligent Distribution Network," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 522-530, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    • Handle: RePEc:zbw:entr20:224718
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    References listed on IDEAS

    as
    1. Bittante, A. & Pettersson, F. & Saxén, H., 2018. "Optimization of a small-scale LNG supply chain," Energy, Elsevier, vol. 148(C), pages 79-89.
    2. Elin Halvorsen-Weare & Kjetil Fagerholt, 2013. "Routing and scheduling in a liquefied natural gas shipping problem with inventory and berth constraints," Annals of Operations Research, Springer, vol. 203(1), pages 167-186, March.
    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.
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    More about this item

    Keywords

    Liquified Natural Gas; distribution network; artificial intelligence; reinforcement learning; economic optimization;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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