Stelios Bekiros, University of Turin

Andrea D'Ariano, Roma Tre University

Peng Wu, Fuzhou University

Guowei Zhang, Dalian University of Technology

Modern operations research increasingly involves complex decision-making in data-rich environments where contextual information can significantly improve operational policies. This special issue focuses on integrating side information into optimization frameworks, combining advances from optimization, machine learning, and statistical learning. The challenge is to develop methods that go beyond historical averages to create adaptive, personalized solutions that effectively handle high-dimensional data, distribution shifts, and real-time computational demands.

The special issue welcomes contributions that develop novel theories, algorithms, and practical applications merging optimization with machine learning and data analytics. Priority is given to methods that address uncertainty while leveraging contextual information, with emphasis on improving actual decision quality and system performance rather than predictive accuracy alone.

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

Potential topics

• Contextual Stochastic Optimization
• Distributionally Robust Optimization with Side Information
• Prescriptive Analytics and Contextual Decision Making
• Learning-Enhanced Optimization
• Decision-Focused Learning
• Online, Dynamic, and Adaptive Optimization
• Learning-Augmented Algorithms
• Interpretable and Trustworthy Optimization Models
• Statistical Guarantees and Generalization in Optimization
• Data-Driven Optimization Under Uncertainty
• AI and Machine Learning for OR
• Human-in-the-Loop Optimization
• Optimization with Foundation Models or Generative AI
• Scalable Algorithms for Large-Scale Optimization Problems
• Digital Twin and Real-Time Decision Systems
• Contextual Transportation and Logistics Optimization
• Contextual Supply Chain and Revenue Management
• Data-Driven Healthcare and Service Operations

Timeline

• July 31, 2027: Submission deadline

Yi-Kuei Lin, National Yang Ming Chiao Tung University

This special issue seeks contributions on reliability modeling and decision support for industrial engineering systems, including manufacturing, supply chains, logistics, energy systems, transportation, and service operations. While traditional probabilistic and stochastic approaches remain important, advances in sensor technologies and data collection have enabled new data-driven approaches that combine machine learning and statistical methods with classical reliability theory.

The special issue welcomes both theoretical developments and practical applications that demonstrate how data-driven reliability analysis can enhance decision-making in maintenance planning, resource allocation, system design, and operational optimization. Papers should show methodological rigor and clear relevance to real-world industrial challenges, illustrating how reliability-informed approaches improve system performance.

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

Potential topics

• Data-driven reliability modeling and parameter estimation using operational data
• Reliability analysis using statistical learning and machine learning methods
• Reliability models combining analytical formulations with data-driven learning approaches
• Decision support systems incorporating reliability analysis and risk information
• Optimization and decision-making problems informed by reliability performance
• Predictive maintenance and condition monitoring using data and learning models
• Multistate system and network reliability analysis
• Reliability evaluation of manufacturing, logistics, and supply chain systems
• Reliability modeling in energy, power, and infrastructure systems
• Stochastic modeling and uncertainty analysis in data-driven reliability studies
• Simulation, enumeration, and approximation methods for reliability evaluation
• System design and resource allocation considering reliability and operational data
• Reliability analysis supported by digital twins, data analytics, and intelligent systems
• Reliability, robustness, and resilience of industrial engineering systems

Timeline

• July 31, 2027: Submission deadline