Cognitive-Aware Shift Scheduling in Smart Manufacturing: An AI Framework for Reducing Burnout and Fatigue and Investigating the Impact of AI-Optimized Work Scheduling and Task Allocation on the Cognitive Load and Emotional Well-Being of Manufacturing Employees
Citation. Mokashi, S. P., & Naik, S. (2025). Cognitive-Aware Shift Scheduling in Smart Manufacturing: An AI Framework for Reducing Burnout and Fatigue and Investigating the Impact of AI-Optimized Work Scheduling and Task Allocation on the Cognitive Load and Emotional Well-Being of Manufacturing Employees. Frontiers in Engineering and Artificial Intelligence and Machine Learning, 2(8), 16–29. https://doi.org/10.37547/feaiml/Volume02Issue08-03
Summary. This article presents an AI-enabled framework for shift scheduling and task allocation that incorporates proposed cognitive and emotional worker-state information. It positions workload-aware scheduling as a way to balance operational efficiency against burnout, fatigue, cognitive load, and well-being risks.
Key claims
- Shift and task allocation systems that optimize only production objectives can overlook worker cognitive load, fatigue, and emotional well-being.
- The proposed framework integrates worker-state indicators into scheduling and allocation decisions rather than treating people as interchangeable capacity.
- AI-supported scheduling requires safeguards around data use, fairness, transparency, and human oversight when it influences worker conditions.
Evidence & limitations
- The source presents a framework and literature-based argument; it does not provide a field evaluation establishing that the proposed approach reduces burnout or improves production outcomes.
- Publication venue and methodological claims warrant cautious use; the record is retained as a public conceptual contribution rather than strong causal evidence.