Artificial Intelligence for Crew Operations Optimization: A Novel Approach to Passenger Transportation

Abstract This paper presents a case study on enhancing offshore helicopter passenger transportation using artificial intelligence (AI), focusing on refining fleet management, route optimisation, and sustainability. The methodology integrates extensive data analysis, algorithm development, and real-world testing to optimize helicopter schedules and reduce carbon footprints. By correcting fleet status inaccuracies and incorporating variables such as shuttle flights and maintenance schedules, the study develops a dynamic scheduling algorithm that prevents time overlaps and responds efficiently to real-time operational changes. Results indicate that AI-driven solutions can significantly improve operational efficiency—optimising crew assignments and departure intervals, reducing delays by up to 15.70%, and cutting fuel consumption by 8.10%, which also decreases CO2 emissions. This novel approach not only enhances operational efficiency and environmental sustainability but also offers a scalable model for future innovations in offshore transportation logistics.