Optimizing a European Small-Scale LNG Supply Chain with Environmental Stewardship - Laurêncio, Tiago Nunes Sim Sim (2023)
In the context of growing global energy uncertainty, small LNG supply chains have emerged as an effective strategy to mitigate the impacts of disruptions in countries' energy supply chains. The flexibility and adaptability of these small supply chains are crucial in dealing with variability in global energy markets, allowing nations to maintain a continuous and reliable supply of LNG, even in the face of unforeseen challenges. This dissertation introduces a mathematical model designed to optimize the small supply chain of LNG, ensuring efficient distribution from suppliers to consumers through a diverse fleet of ships. Formulated as a mixed-integer linear programming problem, the model minimizes fuel procurement costs while meeting consumer demands within a specified time period. It identifies optimal fleet com- position, maritime routes, number of voyages per ship, and shiploads. This research focuses on a West- ern European context, where three potential supply ports in Spain met the LNG demands of five Euro- pean ports. The study solved a base case and conducted a thorough sensitivity analysis, exploring var- iations in LNG prices in different supply ports, carbon taxes, carbon restrictions, berthing times, and demand. The study swiftly explored solution sensitivity, providing valuable insights for decision-mak- ers. The problem was solved using Fico Xpress as the optimization engine and Python as the program- ming language. From a broad perspective, the model produced feasible solutions in a relatively short processing time, demonstrating its value in addressing the LNG procurement problem. The research emphasized its significance by demonstrating the ability to simulate scenarios and draw conclusions when altering real-world parameter values. This highlights its practical importance in tactical and operational man- agement, making it a valuable asset for handling real-life scenarios effectively.