Modern operations research increasingly relies on contextual information to enhance decision-making in uncertain environments. This special issue focuses on the integration of side information—such as demand patterns, user behaviors, and market conditions—into optimization models to achieve more adaptive and data-driven policies. The field spans contextual stochastic optimization, distributionally robust optimization with covariates, decision-focused learning, and online optimization frameworks.

Real-world applications present significant challenges including high-dimensional data, noisy observations, distribution shifts, computational constraints, and the need to balance predictive accuracy with operational performance. The special issue seeks high-quality contributions that develop novel theories, algorithms, and applications combining optimization with machine learning, statistics, and data analytics to address these challenges systematically.

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