The maritime and logistics industries are under increasing pressure to reduce greenhouse gas emissions while maintaining operational efficiency and cost-effectiveness. Fuel consumption constitutes one of the largest operational expenses and environmental effects of fleet operations. Traditional optimization and prediction methods often struggle with the high-dimensional, non-linear, and multi-objective nature of green fleet management, especially when integrating alternative fuels, varying vesse
This problem focuses on developing a quantum-inspired optimization and prediction framework for green fleet management.
Fuel consumption constitutes one of the largest operational expenses and environmental effects of fleet operations Traditional optimization and prediction methods often struggle with the high-dimensional, non-linear, and multi-objective nature of green fleet management, especially when integrating alternative...
This problem focuses on developing a quantum-inspired optimization and prediction framework for green fleet management. The framework will predict fuel consumption under varying operational conditions and optimize fleet deployment decisions, including the selection of vessel types, capacities, cruising speeds, and the integration of alternative fuels (LNG, methanol, hydrogen, ammonia) and shore power solutions. The goal is to minimize fuel consumption and lifecycle emissions while satisfying cargo demand, schedule reliability, and operational constraints. Objectives • Develop accurate quantum-inspired models for predicting fuel consumption across different vessel types and operating conditions. • Design a quantum metaheuristic optimization framework to determine the optimal mix of vessel t