Multistart Large Neighborhood Search for the liquefied natural gas transportation and trading over long-term time horizons

TL;DR

This paper introduces a Multistart Large Neighborhood Search heuristic for optimizing LNG transportation over long-term horizons, effectively handling complex constraints and outperforming baseline models with significant efficiency.

Contribution

The study presents a novel heuristic combining tensor-train optimization and large neighborhood search for LNG transportation planning, addressing complex real-world constraints.

Findings

Outperforms baseline linear-programming model by 35% on historic data

Achieves 44% improvement on artificially generated data

Solution times are only several minutes

Abstract

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 Multistart Large Neighborhood Search heuristic 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. A tensor-train optimizer defines the parameters of Mixed Integer Programming (MIP) models, allowing better solution space exploration. On the historic and artificially generated data, our approach outperforms the baseline linear-programming model by 35% and 44%, respectively, while the time overhead is only several minutes.