International Journal of Production Research

AI-driven Human-Robot Collaboration in Intralogistics Systems

Intralogistics refers to the planning, management, and optimization of material and information flows within facilities such as warehouses, distribution centers, manufacturing plants, container terminals, and emerging retail stores (de Koster, 2025; Fragapane et al., 2021; Cerqueus et al., 2025). The performance of intralogistics operations is crucial for ensuring supply chain execution and meeting customer expectations, as it directly affects order accuracy, lead times, inventory availability, and service quality (Boysen and de Koster, 2025; Dolgui et al., 2025; Li et al., 2023). Traditionally, intralogistics systems have been highly labor-intensive in activities including receiving, storage, picking, packing, sorting, replenishment, and internal transport (Perotti et al., 2025; Dolgui and Ivanov, 2025). These operations rely on large workforces to perform repetitive and physically demanding tasks under time pressure. However, growing market dynamics, such as the surge in e-commerce, fragmented and small-batch orders, and the need for instant and same-day delivery, have intensified operational complexity while exacerbating labor shortages and rising costs. Consequently, the integration of robotics into intralogistics has become a strategic imperative.

Technologies such as automated guided vehicles (AGVs), autonomous mobile robots (AMRs), and various robotic arms are widely deployed to support material handling and assembly tasks in production intralogistics (Nourmohammadi et al., 2025). Besides, warehouse operations have seen rapid adoption of robotic mobile fulfillment systems (RMFS) and other robot-assisted order picking systems aimed at improving productivity (Ma et al, 2023, 2025). However, despite these advancements, many intralogistics operations remain beyond the reach of full automation, particularly when cost-effectiveness and operational flexibility are taken into account (de Koster and Roy, 2024; Pasparakis et al.,2023). Therefore, human workers continue to play a central role in a robotized system that requires dexterity, contextual decision-making, and adaptability. This reality underscores the growing importance of human-robot collaboration (HRC), wherein robot autonomy and human capability are harmonized to create synergistic, adaptive systems that leverage the advantages of the two parties (Ma and Saenz, 2025), for example, combining the speed and accuracy of robots with human flexibility.

While HRC has improved operational performance in many intralogistics systems, there is a long way to go to realize its full potential (Ma and Saenz, 2025). For example, order pickers in RMFS complain that they struggle to keep pace with the relentless speed of robots, which results in significant physical strain and serious injuries. This can also erode their trust in and willingness to collaborate with robots, and robots themselves are often underutilized, ultimately hurting system efficiency. While robots outperform humans in speed and precision, without effective collaboration, these advantages can be lost to system inefficiencies, human dissatisfaction, and workplace hazards. To address these challenges, AI technologies, such as large language models (LLMs), multi-agent reinforcement learning, and advanced computer vision, have emerged as a pivotal driver for advancing the performance of HRC in terms of contextualization, communication, customization, task performance, and continuous improvement (Ma and Saenz, 2025).

This Special Issue aims to gather cutting-edge research that explores how AI technologies, ranging from machine learning and computer vision to LLMs and reinforcement learning, can enhance HRC in intralogistics environments. We welcome both theoretical and empirical studies, as well as interdisciplinary approaches that bridge engineering, operations management, ergonomics, and cognitive science.

We invite high-quality submissions on (but not limited to) the following topics:

• AI-driven planning and control for human-robot collaborative intralogistics operations.
• Real-time decision-making and task allocation in shared human-robot workspaces.
• Safety, trust, and explainability of AI in HRC environments.
• Adaptive interfaces and communication between humans and robots using AI-driven perception and language models.
• Digital twins and simulation-based optimization for AI-enabled HRC systems.
• Human factors, ergonomics, and cognitive workload in AI-supported HRC.
• Learning-based methods for robot training and co-adaptation with human workers.
• Applications of generative AI and LLMs for HRC in warehouses.
• Case studies and industrial implementations of AI-powered HRC in intralogistics.
• Ethical, social, and organizational implications of AI in human-robot collaborative environments.

Guest editors:

• Benedict Jun Ma, Assistant Professor, Hong Kong University of Science and Technology (Guangzhou), China [Managing Guest Editor]
• George Q. Huang, Chair Professor, The Hong Kong Polytechnic University, Hong Kong SAR
• René de Koster, Professor, Erasmus University, Rotterdam, Netherlands
• Yong-Hong Kuo, Associate Professor, The University of Hong Kong, Hong Kong SAR
• Shenle Pan, Professor, Mines Paris, PSL University, France
• Ilya Jackson, Research Scientist, Massachusetts Institute of Technology, Cambridge, United States

References:

Boysen, N., & De Koster, R. (2025). 50 years of warehousing research—An operations research perspective. European Journal of Operational Research, 320(3), 449-464.

Cerqueus, A., Dolgui, A., Ivanov, D., Klimtchik, A., Lemoine, D., & Pashkevich, A. (2025). Applications of artificial intelligence in industry 4.0 and smart manufacturing. Engineering Applications of Artificial Intelligence, 149, 110509.

de Koster, R., Roy, D., Lim, Y. F., & Kumar, S. (2025). IoT in intralogistics: Applications and emerging research. Production and Operations Management.

de Koster, R., & Roy, D. (2024). Research: Warehouse and logistics automation works better with human partners. Harvard Business Review, June 21, 2024, https://hbr.org.

Dolgui, A., & Ivanov, D. (2024). Internet of behaviors: conceptual model, practical and theoretical implications for supply chain and operations management. International Journal of Production Research, 63(1), 1–8. https://doi.org/10.1080/00207543.2024.2372008.

Dolgui, A., Ivanov, D., & Simchi-Levi, D. (2025). Stress tests for supply chains: towards resilience and viability. International Journal of Production Research, 63(9), 3254–3258. https://doi.org/10.1080/00207543.2025.2483113.

Fragapane, G., De Koster, R., Sgarbossa, F., & Strandhagen, J. O. (2021). Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda. European Journal of Operational Research, 294(2), 405-426.

Li, M., Guo, D., Li, M., Qu, T., & Huang, G. Q. (2022). Operation twins: production-intralogistics synchronisation in Industry 4.0. International Journal of Production Research, 61(15), 5193–5211. https://doi.org/10.1080/00207543.2022.2098874.

Ma, B. J., Pan, S., Zou, B., Kuo, Y. H., & Huang, G. Q. (2025). Operating policies for robotic cellular warehousing systems. Transportation Research Part E: Logistics and Transportation Review, 194, 103875.

Ma, B. J., Kuo, Y. H., Jiang, Y., & Huang, G. Q. (2023). RubikCell: Toward robotic cellular warehousing systems for e-commerce logistics. IEEE Transactions on Engineering Management, 71, 9270-9285.

Ma, B. J., & Saenz, M. J. (2025). AI Can Improve How Humans and Robots Work. MIT Sloan Management Review. https://doi.org/10.63383/DAfd5096

Nourmohammadi, A., Arbaoui, T., Fathi, M., & Dolgui, A. (2025). Balancing human–robot collaborative assembly lines: A constraint programming approach. Computers & industrial engineering, 111154.

Pasparakis, A., De Vries, J., & De Koster, R. (2023). Assessing the impact of human–robot collaborative order picking systems on warehouse workers. International Journal of Production Research, 61(22), 7776–7790. https://doi.org/10.1080/00207543.2023.2183343.

Perotti, S., Cannava, L., Ries, J. M., & Grosse, E. H. (2024). Reviewing and conceptualising the role of 4.0 technologies for sustainable warehousing. International Journal of Production Research, 63(6), 2305–2337. https://doi.org/10.1080/00207543.2024.2396015.