AI-Based Scheduling for Cost-Effective Maritime Energy Management

This study investigates the role of artificial intelligence-based scheduling in optimizing maritime energy management systems to enhance sustainability and cost efficiency. Using quantitative analysis derived from real-time operational data of 15 international shipping routes between 2015 and 2024, the research applies multi-vector energy optimization models that integrate machine learning-based predictive scheduling, port turnaround time analysis, and adaptive fuel management algorithms. The empirical findings indicate that artificial intelligence scheduling reduces overall operational energy consumption by 14–18% and improves system reliability by approximately 25%, while achieving significant reductions in carbon intensity compared with conventional scheduling practices. Regression and sensitivity analyses confirm that adaptive optimization in voyage planning contributes directly to both financial and environmental performance improvement. The study concludes that AI-driven scheduling frameworks provide a measurable pathway toward achieving International Maritime Organization decarbonization targets and ensuring economically viable shipping operations in a competitive global environment.