Small-scale LNG Market Optimization – Intelligent Distribution Network

  • Edyta Kuk AGH University of Science and Technology, Poland
  • Bartłomiej Małkus AGH University of Science and Technology, Poland
  • Michał Kuk AGH University of Science and Technology, Poland
Keywords: Liquified Natural Gas, distribution network, artificial intelligence, reinforcement learning, economic optimization

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.

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References

Alvarez, J. A. L., Buijs, P., Deluster, R., Coelho, L. C., Ursavas, E. (2019), “Strategic and operational decision-making in expanding supply chains for LNG as a fuel”, Omega, 102093, Article in press, Corrected proof.

Bittante, A., Pettersson, F., Saxén, H. (2018), “Optimization of a small-scale LNG supply chain”, Energy, Vol. 148, pp. 79-89.

Braekers, K., Ramaekers, K., Van Nieuwenhuyse, I. (2016), “The vehicle routing problem: state of the art classification and review”, Computers & Industrial Engineering, Vol. 99, pp. 300-313.

Burggräf, P., Wagner, J., Koke, B. (2018), “Artificial intelligence in production management: a review of the current state of affairs and research trends in academia”, 2018 International Conference on Information Management and Processing ICIMP, 12-14 January, IEEE, London, pp. 82-88.

Halvorsen-Weare, E. E., Fagerholt, K. (2013), “Routing and scheduling in a liquefied natural gas shipping problem with inventory and berth constraints”, Annals of Operations Research, Vol. 203, No. 1, pp. 167-186.

Jain, L. C., Seera, M., Lim, C. P., Balasubramaniam, P. (2014), “A review of online learning in supervised neural networks”, Neural Computing and Applications, Vol. 25, No. 3-4, pp. 491-509.

Jokinen, R., Pettersson, F., Saxén, H. (2015), “An MILP model for optimization of a small-scale LNG supply chain along a coastline”, Applied Energy, Vol. 138, pp. 423-431.

Kaelbling, L., Littman, M., Moore, A. (1996), “Reinforcement learning: a survey”, Journal of Artificial Intelligence Research, Vol. 4, pp.237-285.

Li, W., Li, Y. W., Wu, Q. S. (2013), “Modeling and control of complex industrial processes using artificial intelligence techniques”, 2013 International Conference on Machine Learning and Cybernetics, 14-17 July, IEEE, Tianjin, China, pp. 1341-1345.

Nazari, M., Oroojlooy, A., Snyder, L., Takáč, M. (2018), “Reinforcement learning for solving the vehicle routing problem”, in Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (Eds.), Advances in Neural Information Processing Systems NIPS 2018, 3-8 December, Neural Information Processing Systems Foundation, Montreal, Canada, pp. 9839-9849.

Riaz, F., Ali, K. M. (2011), “Applications of graph theory in computer science”, In Third International Conference on Computational Intelligence, Communication Systems and Networks, 26-28 July, IEEE, Bali, Indonesia, pp. 142-145.

Shteimberg, E., Kravits, M., Ellenbogen, A., Arad, M., Kadmon, Y. (2012), “Artificial intelligence in nonlinear process control based on fuzzy logic”, 27th Convention of Electrical and Electronics Engineers in Israel, 14-17 November, IEEE, Eilat, Israel, pp. 1-5).

Published
2020-09-22
How to Cite
Kuk, E., Małkus, B., & Kuk, M. (2020). Small-scale LNG Market Optimization – Intelligent Distribution Network. Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (Online), 6(1), 522-530. Retrieved from https://proceedings.entrenova.org/entrenova/article/view/353
Section
Economic Development, Innovation, Technological Change, and Growth