Hyun-Jung Kim, KAIST

Shu-Kai Fan, National Taipei University of Technology

Fugee Tsung, Hong Kong University of Science and Technology

Thomas Volling, Technical University Berlin

Jang Ho Kim, Korea University

Dong-Young Lim, Ulsan National Institute of Science and Technology

This special issue addresses decision-making in complex industrial systems facing dynamic uncertainty through artificial intelligence and optimization techniques. The issue seeks research combining AI methods such as reinforcement learning, generative AI, and digital twins with operations research and optimization approaches to manage manufacturing, supply chains, healthcare, and other sectors dealing with demand fluctuations and disruptions.

The special issue welcomes both theoretical and practical contributions demonstrating how AI-driven methodologies can improve decision-making under uncertainty. Particular emphasis is placed on interdisciplinary research with clear industrial applicability and the adoption of open science practices including data and code sharing to enhance reproducibility.

Appel publié par International Journal of Production Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• AI-driven decision-making under uncertainty
• Production planning and scheduling in stochastic and dynamic environments
• Reinforcement learning for uncertain industrial systems
• Stochastic optimization and robust operational strategies
• AI-enabled statistical quality control and process improvement
• Hybrid AI and optimization approaches for uncertain environments
• Data-driven optimization and prescriptive analytics
• AI-enhanced supply chain and logistics management under disruptions
• Real-time and adaptive decision-making systems
• Simulation-based optimization and digital twins under uncertainty
• Agentic AI and autonomous industrial systems
• Explainable and trustworthy AI for operational decision-making
• AI for resilient and sustainable operations
• Industrial applications and case studies of AI-driven decision systems

Timeline

• October 1, 2026: Submissions open
• January 31, 2027: Submission deadline

Bernie F. Quiroga, West Virginia University

Xuying Zhao, Texas A&M University

Oleg Gusikhin, Ford Motor Company

Andrea Matta, Politecnico di Milano

Yong Yin, Doshisha Business School

This special issue honors Professor Kathryn E. Stecke's 75th birthday by showcasing research that builds upon her substantial contributions to production research, flexible manufacturing systems, supply chain management, and the interface between production and marketing. The issue seeks to curate cutting-edge articles in active and emerging areas that reflect her legacy and influence.

The special issue welcomes high-quality submissions including empirical studies, analytical models, simulation-based research, case analyses, and conceptual papers. Manuscripts should demonstrate rigorous, impactful research with comprehensive literature reviews, novel decision-aid models, comparisons with state-of-the-art methods, discussions of real-life applications, and managerial insights.

Appel publié par International Journal of Production Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Advances in flexible manufacturing systems (FMSs), including machine grouping, loading policies, and production ratio optimization for enhanced utilization
• Interfaces between production and marketing, including focal points such as advance selling strategies, pre-order mechanisms considering consumer behavior (e.g., loss aversion), lead time and price quotation modes, production-transportation integration in make-to-order systems, and competition/coordination in online marketplaces
• Reconfigurable production systems, such as Seru, for improving responsiveness and adaptability in volatile environments
• Supply chain resilience strategies, including adaptive ordering, disruption mitigation, and multi-echelon network design amid geopolitical and operational uncertainties
• Production planning and scheduling in capital-intensive systems with machine flexibility, focusing on hierarchical approaches and real-time control
• Integration of digital technologies (e.g., AI, digital twins, and data analytics) in manufacturing and supply chains to boost efficiency and cost control
• Empirical analyses of manufacturing efficiencies in sectors like semiconductors, automotive, and electronics, drawing on case studies of disruption responses
• Comparative studies of traditional vs. innovative production methods, such as Toyota Production System vs. Seru, with emphasis on performance metrics
• Managerial insights for decision-makers on implementing flexible routing, robot scheduling, and state-dependent sequencing in production environments
• Future perspectives on sustainable manufacturing, addressing global supply chain vulnerabilities and policy implications

Timeline

• March 1, 2026: Open for submissions
• October 31, 2026: Submission deadline