Abstract
Mobility is essential for the growth of a society as it enables accessibility to opportunities, goods and services. However, one area where mobility is lacking is in healthcare, due to a lack of accessible transportation, resulting in high morbidity and mortality rates. This proposal aims to develop comprehensive and innovative optimization models for Non-Emergency Medical Transport (NEMT), addressing the lack of structured approaches and complete problem integration in the current state-of-the-art. The project will focus on integrating shift management, real-time re-optimization, and full-system modeling to enhance efficiency and service quality. The candidate's strong background in Operations Research (OR), developed through a Master's degree from Ghent University, a PhD focused on on-demand transportation, and professional experience in the sector, effectively bridges the gap between theoretical knowledge and real-world application. Using both synthetic and real data, benchmark instances will be generated and made publicly available to ensure reproducibility. Advanced algorithms, including (meta)heuristics, game theory and machine learning, will be employed to develop robust, adaptable solutions. The outcome of this research is to set a new standard for NEMT optimization and improve overall transportation in healthcare.
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