Olga Battaïa, Kedge Business School

Yasser Dessouky, San Jose State University

Reza Zanjirani Farahani, Paris School of Business

Masood Fathi, University of Skövde

Madjid Tavana, La Salle University

This special issue investigates how collaborative intelligence—combining human expertise, artificial intelligence, and distributed problem-solving—can advance operations research to address modern decision-making challenges. Traditional OR methods struggle with the dynamic and interconnected nature of contemporary problems, from logistics to emergency response. The collection seeks contributions that demonstrate how human-AI collaboration can enhance decision-making, boost system resilience, and optimize complex operational environments.

The call invites theoretical, computational, and applied research on human-AI collaboration in OR models, optimization and game-theoretic approaches for multi-agent systems, adaptive and decentralized frameworks, and data-driven learning-based optimization. Submissions should demonstrate both theoretical rigor and practical relevance, with innovative methodologies and real-world applications that advance collaborative intelligence in operations research.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Designing OR models that facilitate seamless interaction and information exchange between human decision-makers and AI agents
• The framework for integrating human judgment, preferences, and ethical considerations into AI-driven decision-making
• Techniques for visualizing and interpreting AI outputs to enhance human understanding and trust
• Novel optimization algorithms and game-theoretic frameworks for coordinating and optimizing decisions in multi-agent environments
• Models addressing diverse objectives, capabilities, and interactions among multiple agents
• Approaches to managing conflicts, uncertainties, and strategic behaviors in multi-agent decision-making
• OR frameworks capable of dynamically adapting to real-time changes and uncertainties
• Decentralized optimization algorithms and control strategies for distributed systems
• Online learning and adaptive control techniques to improve system responsiveness and resilience
• Leveraging machine learning and data analytics to extract insights and patterns for OR applications
• Learning-based optimization algorithms that improve performance through data feedback
• Predictive analytics and simulation techniques for enhanced decision-making and risk management

Timeline

• December 31, 2026: Submission deadline