Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
Authors:
Alexander Nasuta,
Alessandro Cisi,
Sylwia Olbrych,
Gustavo Vieira,
Rui Fernandes,
Lucas Paletta,
Marlene Mayr,
Rishyank Chevuri,
Robert Woitsch,
Hans Aoyang Zhou,
Anas Abdelrazeq,
Robert H. Schmitt
Abstract:
This work presents a two-layer, human-centric production planning framework designed to optimize both operational efficiency and workforce fairness in industrial manufacturing. The first layer formulates the Order-Line allocation as a Constraint Programming (CP) problem, generating high-utilization production schedules that respect machine capacities, processing times, and due dates. The second la…
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This work presents a two-layer, human-centric production planning framework designed to optimize both operational efficiency and workforce fairness in industrial manufacturing. The first layer formulates the Order-Line allocation as a Constraint Programming (CP) problem, generating high-utilization production schedules that respect machine capacities, processing times, and due dates. The second layer models Worker-Line allocation as a Markov Decision Process (MDP), integrating human factors such as worker preference, experience, resilience, and medical constraints into the assignment process. Three solution strategies, greedy allocation, MCTS, and RL, are implemented and compared across multiple evaluation scenarios. The proposed system is validated through 16 test sessions with domain experts from the automotive industry, combining quantitative key performance indicators (KPIs) with expert ratings. Results indicate that the CP-based scheduling approach produces compact, feasible production plans with low tardiness, while the MDP-based worker allocation significantly improves fairness and preference alignment compared to baseline approaches. Domain experts rated both the Order-Line and Worker-Line components as effective and highlighted opportunities to further refine the objective function to penalize excessive earliness and improve continuity in worker assignments. Overall, the findings demonstrate that combining CP with learning-based decision-making provides a robust approach for human-centric production planning. The approach enables simultaneous optimization of throughput and workforce well-being, offering a practical foundation for fair and efficient manufacturing scheduling in industrial settings.
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Submitted 1 October, 2025;
originally announced October 2025.
Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
Authors:
Vivek Chavan,
Arsen Cenaj,
Shuyuan Shen,
Ariane Bar,
Srishti Binwani,
Tommaso Del Becaro,
Marius Funk,
Lynn Greschner,
Roberto Hung,
Stina Klein,
Romina Kleiner,
Stefanie Krause,
Sylwia Olbrych,
Vishvapalsinhji Parmar,
Jaleh Sarafraz,
Daria Soroko,
Daksitha Withanage Don,
Chang Zhou,
Hoang Thuy Duong Vu,
Parastoo Semnani,
Daniel Weinhardt,
Elisabeth Andre,
Jörg Krüger,
Xavier Fresquet
Abstract:
This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI systems that simulate or interpret human emotions are reshaping our interactions in areas such as education, healthcare, mental health, caregiving, and digital life…
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This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI systems that simulate or interpret human emotions are reshaping our interactions in areas such as education, healthcare, mental health, caregiving, and digital life. The analysis is structured around four central themes: the ethical implications of emotional AI, the cultural dynamics of human-machine interaction, the risks and opportunities for vulnerable populations, and the emerging regulatory, design, and technical considerations. The authors highlight the potential of affective AI to support mental well-being, enhance learning, and reduce loneliness, as well as the risks of emotional manipulation, over-reliance, misrepresentation, and cultural bias. Key challenges include simulating empathy without genuine understanding, encoding dominant sociocultural norms into AI systems, and insufficient safeguards for individuals in sensitive or high-risk contexts. Special attention is given to children, elderly users, and individuals with mental health challenges, who may interact with AI in emotionally significant ways. However, there remains a lack of cognitive or legal protections which are necessary to navigate such engagements safely. The report concludes with ten recommendations, including the need for transparency, certification frameworks, region-specific fine-tuning, human oversight, and longitudinal research. A curated supplementary section provides practical tools, models, and datasets to support further work in this domain.
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Submitted 14 June, 2025;
originally announced June 2025.