Fatigue-Aware Sub-Second Combinatorial Auctions for Dynamic Cycle Allocation in Human–Robot Collaborative Assembly

Problem: Existing Human–Robot Collaboration (HRC) allocators cannot react at a sub‑second scale while accounting for worker fatigue. Objective: We designed a fatigue‑aware combinatorial auction executed every 100 ms. Method: A human and a FANUC robot submit bids combining execution time, predicted energy, and real‑time fatigue; a greedy algorithm (≤1 ms) with a approximation guarantee and O (|Bids| log |Bids|) complexity maximizes utility. Results: In 1000 RoboDK episodes, the framework increases active cycles·min⁻¹ by 20%, improves robot utilization by +10.2 percentage points, reduces per cycle fatigue by 4%, and raises the collision‑free rate to 99.85% versus a static baseline (p < 0.001). Contribution: We provide the first transparent, sub‑second, fatigue‑aware allocation mechanism for Industry 5.0, with quantified privacy safeguards and a roadmap for physical deployment. Unlike prior auction-based or reinforcement learning approaches, our model uniquely integrates a sub-second ergonomic adaptation with a mathematically interpretable utility structure, ensuring both human-centered responsiveness and system-level transparency.