Quantum-enhanced Optimization for LNG Ship Routing: Integrating Digital Twins with QAOA

Liquefied natural gas (LNG) shipping poses unique optimization challenges in global logistics due to strict operational constraints, dynamic environmental conditions, and the need to minimize both costs and delays. This study explores the integration of quantum optimization and digital twin technology to address the complexities of LNG ship routing. Specifically, we formulate the ship routing problem as a mixed-integer linear programming (MILP) problem aimed at minimizing costs and travel time while incorporating constraints such as ship capacities and weather variability. Given the NP-hard nature of the problem, classical optimization methods struggle to scale efficiently. To overcome this, we transform the MILP into a quadratic unconstrained binary optimization (QUBO) format and employ the quantum approximate optimization algorithm (QAOA) to solve it. The proposed framework integrates real-time data via digital twins, enabling dynamic updates to the optimization process.