Madjid Tavana, La Salle University

Olga Battaïa, KEDGE Business School

Yasser Dessouky, San Jose State University

Masood Fathi, University of Skövde

Reza Zanjirani Farahani, Paris School of Business

The increasing complexity of modern decision-making demands advanced Operations Research (OR) models that integrate Collaborative Intelligence—the synergy between human expertise, Artificial Intelligence (AI)-driven decision support, and distributed problem-solving frameworks. This paradigm enhances adaptability, efficiency, and resilience in complex operations by leveraging multi-agent coordination, human-AI collaboration, decentralized optimization, and machine learning-enhanced decision-making.

Traditional OR methodologies, despite their strengths in static environments, struggle with the dynamic, interconnected, and uncertain nature of modern decision-making. As Russell Ackoff noted in his 1956 article, "The Aging of a Young Profession," OR was already showing signs of stagnation, becoming overly preoccupied with mathematical techniques rather than addressing real-world problems holistically. By 1979, in "The Future of Operational Research is Past," he further criticized OR for prioritizing optimization within narrow constraints instead of embracing a systemic, interdisciplinary approach. Modern applications like logistics, production, service systems, and emergency response require more adaptive and interactive OR models. Collaborative Intelligence offers a transformative approach, enabling real-time interaction among humans, AI agents, and mathematical models to optimize problem-solving and system performance.

The rapid advancements in AI have revolutionized decision-making across OR domains. However, fully autonomous AI systems face challenges in handling uncertainty, ethical considerations, and interpretability, particularly in high-stakes environments. Collaborative Intelligence bridges this gap by combining the computational power of AI with human intuition, adaptability, and ethical reasoning, fostering trust and robustness in decision-making.

This special issue explores cutting-edge methodologies, theoretical advancements, and practical applications of Collaborative Intelligence in OR. We invite high-quality contributions that address how human expertise and AI can collaboratively enhance decision-making, improve system resilience, and optimize complex operational environments. We welcome original research contributions that propose innovative mathematical models, algorithms, and applications of OR for collaborative decision-making, resilience planning, decentralized optimization, and dynamic problem-solving in complex operational environments. Submissions should demonstrate theoretical rigor and practical relevance, focusing on advancing the state of the art in Collaborative Intelligence.

This special issue aims to provide a platform for researchers and practitioners to share insights, methodologies, and case studies that highlight the transformative potential of Collaborative Intelligence in OR.

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