Quantum Computing and Machine Learning for Efficiency of Maritime Container Port Operations

Abstract

Maritime container ports are experiencing a variety of challenges, including the pandemic and other stressors, that are altering perspectives on efficiency, risk, and resilience. This study reviews new methods of operations optimization that serve major goals of logistics systems: Increasing energy and time efficiencies and reducing emissions and congestion. Several computational methods will be assessed, including quantum computing, neural networks, and operations heuristics. The methods are compared by potential for increased efficiencies, including the increase in container volumes, reduction of dwell times, reduction of container moves, utilization of demand forecasts, and decreases in emissions. The results suggest opportunities for reinforcement learning to improve the scheduling of container transactions across transportation modes, including maritime, truck, rail, crane, and barge.