AI-Driven Digital Twins for Autonomous and Coordinated Decision-Making in Hospital Pharmaceutical Networks

Hospital pharmaceutical networks are a critical, and chronically under-optimised, component of healthcare systems. Drug shortages, pharmaceutical waste, near-expiry stock, and misallocation of medicines across hospital sites generate significant clinical risks, financial costs, and environmental harm. Although these networks generate large volumes of operational data (dispensing, stock movements, orders, traceability), this data remains largely unexploited for real-time decision support: replenishment and allocation decisions still rely on static rules or individual judgment. This thesis aims to develop an autonomous, data-driven decision intelligence system for interconnected hospital pharmaceutical networks, where a central pharmacy supplies several hospital sites with heterogeneous demand profiles. The project is organised around three complementary axes, progressing from understanding the real network to autonomous decision-making. The first axis builds a process-aware, self-evolving digital twin of the pharmaceutical network from real event logs, relying on Object-Centric Process Mining. The second axis enriches this twin with an AI intelligence layer combining demand prediction, early anomaly detection, and 'What if?' scenario simulation. The third axis constitutes the decision layer: replenishment and allocation policies are learned through reinforcement learning (RL), with a multi-agent extension (MARL) coordinating decisions between the central pharmacy and hospital sites while preserving their local autonomy. The project relies on a structured partnership combining CHU de Toulouse as the operational testbed, the Centre Génie Industriel at IMT Mines Albi as the host laboratory, and international co-supervision with the LR-OASIS Laboratory at the École Nationale d'Ingénieurs de Tunis (ENIT). This Franco-Tunisian dimension builds on a scientific collaboration already active for several years between the supervisory teams. This work will contribute to improving care quality, patient safety, and the sustainability of health systems, positioned at the intersection of industrial engineering, artificial intelligence, and hospital management — in full alignment with the 'Understanding and promoting health and well-being' axis of the BEST doctoral programme. École doctorale : SYSTEMES Direction : Safa LAYEB Financement : MSCA COFUND BEST