Towards Fault-Tolerant AGV Task Scheduling in Flexible Manufacturing Systems Using a Tree-Based Max-Plus Predictive Approach

Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete event system, where standard max-plus algebra captures robot synchronization, and a switching mechanism represents alternative resource assignments. To address real-world operational disturbances, the predictive model is enhanced with a fault-tolerant control (FTC) mechanism that dynamically estimates and adapts to non-stationary transport delays. The resulting decision space, which grows exponentially with the prediction horizon, is explored via a predictive tree search algorithm utilizing a quadratic cost function to penalize excessive and uneven transport times. The physical dispatch layer is realized using KIS.BOX IoT devices acting as operator-controlled stations, communicating with the central controller via a WebSocket/STOMP event stream and a lightweight REST API. Simulation results obtained in a Blender 3D environment demonstrate that the proposed FTC predictive strategy significantly reduces the variance of task completion times under fault conditions compared to a baseline First-In-First-Out approach. Furthermore, the IoT integration successfully simulates and validates the feasibility of human-in-the-loop task injection within a realistic, stochastic scenario.