Digital Twin-Enabled Adaptive Fleet Management

This project develops an operator-centred Digital Twin framework for resilient Autonomous Mobile Robot (AMR) fleet management in dynamic smart manufacturing environments. Building on a multi-fidelity digital architecture that combines Digital Models, Digital Shadows, surrogate-based multi objective optimisation and high-fidelity simulation, the system detects incidents such as AMR failures, machine breakdowns and demand shocks, then generates incident aware schedules in real time. A lightweight surrogate model provides rapid rescheduling within a few seconds while remaining close to high fidelity reference behaviour, enabling throughput and energy efficiency to be preserved under disruption. In parallel, a human in the loop decision layer exposes Pareto fronts, Gantt chart previews and post simulation metrics to operators, who can select preferred trade-offs, request validation via re simulation or issue policy bound overrides, all under role-based access control. Every decision is captured as a machine-readable artefact with timestamps, reason codes and override rationale, creating a single audited trail that supports governance, cross shift handover and future learning from operator choices. The framework will be instantiated and evaluated on a battery module assembly line, providing benchmark evidence on latency, robustness and operator workload for different policy modes prioritising speed, assurance or governance, and delivering a transferable blueprint for Industry 5.0 ready, human centric AMR coordination.