A Parameter-Guided Two-Stage Heuristic for Liquefied Natural Gas Transportation and Trading over Long-Term Horizons

Liquefied natural gas (LNG) transportation is a critical component of the energy industry. It enables the efficient and large-scale movement of natural gas across vast distances by converting it into a liquid form, thereby addressing global demand and connecting suppliers with consumers. In this study, we present the Parameter-Guided Two-Stage Heuristic (PG-TSH) for the LNG transportation problem, which involves hundreds of contracts and a planning horizon of two to three years. Our model incorporates several fuel types, LNG sloshing in the tank, and speed- and load-dependent consumption rates. We also consider flexible contracts with LNG volume variability, enabling volume optimizations and multiple discharges. An outer derivative-free parameter-search routine tunes the coefficients of mixed-integer programming (MIP) models, allowing better solution-space exploration. In the experiments, this routine is instantiated with the tensor-train optimizer. On the historic and artificially generated data, our approach outperforms the baseline linear programming model by 35% and 7–44%, respectively, while the time overhead is only several minutes.