Multistart Large Neighborhood Search for the liquefied natural gas transportation and trading over long-term time horizons
S. Iudin (1), M. Veshchezerova (1), K. Tsarova (1), G. Tadumadze (1), V. Shete (1), J.-K. Hao (2), M. Perelshtein (1) ((1) Terra Quantum AG, (2) LERIA, Universite' d'Angers)

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…
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Taxonomy
TopicsVehicle Routing Optimization Methods · Advanced Multi-Objective Optimization Algorithms · Spacecraft and Cryogenic Technologies
