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

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  • Claudio Urrea

    (Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, Chile)

Abstract

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 1 − 1 / e 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.

Suggested Citation

  • Claudio Urrea, 2025. "Fatigue-Aware Sub-Second Combinatorial Auctions for Dynamic Cycle Allocation in Human–Robot Collaborative Assembly," Mathematics, MDPI, vol. 13(15), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:15:p:2429-:d:1711771
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    References listed on IDEAS

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    1. Linjie Liu & Jixian Zhang & Zhemin Wang & Jia Xu, 2023. "A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration," Mathematics, MDPI, vol. 11(24), pages 1-18, December.
    2. Lemos, Leonardo F.L. & Starke, Allan R. & da Silva, Alexandre K., 2025. "Constrained Gaussian processes as a surrogate model for simulation-based optimization of solar process heat systems," Applied Energy, Elsevier, vol. 395(C).
    3. Anjan Bandyopadhyay & Utkarsh Choudhary & Vaibhav Tiwari & Kaushtab Mukherjee & Sapthak Mohajon Turjya & Naim Ahmad & Abid Haleem & Saurav Mallik, 2025. "Quantum Game Theory-Based Cloud Resource Allocation: A Novel Approach," Mathematics, MDPI, vol. 13(9), pages 1-31, April.
    4. Kirill Kozlov & Guennady Ougolnitsky, 2022. "A Game Theoretic Model of Struggle with Corruption in Auctions: Computer Simulation," Mathematics, MDPI, vol. 10(19), pages 1-11, October.
    5. Weissinger, Frederico & Lacava, Pedro & Peñaranda, Alexander & Martelli, Andre & Rufino, Caio Henrique & Curto-Risso, Pedro & Martinez-Boggio, Santiago, 2025. "Enhancing efficiency of ethanol-powered range extenders in the BMW i3: A simulation-based optimization approach," Renewable Energy, Elsevier, vol. 251(C).
    6. Shanxian Lin & Yifei Yang & Yuichi Nagata & Haichuan Yang, 2025. "Elite Evolutionary Discrete Particle Swarm Optimization for Recommendation Systems," Mathematics, MDPI, vol. 13(9), pages 1-36, April.
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