Fleet Orchestration & Traffic Management Digital Worker

Core Logic

Implements DAG (Directed Acyclic Graph) workflow orchestration with 8 nodes and 12 edges. Manages agent priority scheduling, conflict resolution, and real-time state synchronization. Uses Claude-3.5-Sonnet at temperature 0.3 for deterministic coordination. Tracks execution phases 0-100% with critical path analysis for optimization.

Core Logic

Applies the Hungarian Algorithm (Kuhn-Munkres) with O(n³) complexity for optimal bipartite matching. Constructs multi-objective cost matrices incorporating distance, battery state, current load, and deadline urgency. Achieves 24% cost improvement versus greedy baseline. Example: optimally assigns 3 missions across 12 AGVs in milliseconds.

Core Logic

Implements A* pathfinding with Manhattan heuristic and dynamic congestion weighting at O((V+E)log V) complexity. Processes 8,000-node grids (100×80), exploring ~2,341 nodes per path computation. Incorporates real-time traffic data to avoid bottlenecks. Supports multi-floor routing with elevator/lift transitions. Computes 3 paths at 127m total in 34ms.

Core Logic

Applies Tarjan's Strongly Connected Components algorithm at O(V+E) for deadlock detection in the vehicle dependency graph. Uses swept-sphere collision detection at O(n²) to identify potential intersections. Resolves conflicts via priority-based yield commands with configurable wait times (e.g., 3-second yields). Maintains zero-collision operation.

Core Logic

Implements min-heap based load balancing at O(n log n) to distribute missions evenly across the fleet. Manages intersection time-slot reservations to prevent conflicts. Predicts congestion patterns and proactively reroutes vehicles. Reduces workload standard deviation by 45% (12.3→6.7), dramatically improving fleet utilization uniformity.

Core Logic

Performs State of Charge (SOC) Coulomb counting with energy consumption modeling (0.0015 kWh/m). Predicts mission energy requirements and validates sufficient charge before dispatch. Schedules charging during low-demand windows to maximize availability. Applies battery degradation models to optimize charge cycles and extend battery lifespan.

Core Logic

Deploys LSTM neural networks at O(n×h²) for demand forecasting, predicting surges like +34% volume increases. Runs Isolation Forest at O(n log n) for anomaly detection on AGV telemetry. Calculates Remaining Useful Life (RUL) for predictive maintenance (e.g., AGV-023 RUL: 847 hours). Executes Monte Carlo simulations for scenario planning.

Core Logic

Aggregates data from 847 IoT sensors to maintain a synchronized digital twin with 99.7% accuracy at 8ms latency. Provides real-time visualization of AGV positions, zone occupancy, and equipment status. Runs physics simulations for what-if analysis. Detects state deviations and environmental anomalies for proactive intervention.