Recent Advances in Optimization and Decision Making under Uncertainty: Theory and Algorithms

Éditeurs invités

• Abdel Lisser, Paris-Saclay University
• Jia Liu, Xi'an Jiaotong University
• Francesca Maggioni, University of Bergamo
• S. K. Neogy, Indian Statistical Institute
• Vikas Vikram Singh, Indian Institute of Technology Delhi

Synthèse

This special issue brings together recent advances in optimization and decision-making under uncertainty, focusing on theoretical contributions that strengthen the mathematical foundations and algorithmic techniques in operations research. The scope encompasses developments in stochastic, robust, and distributionally robust optimization, game theory, sequential decision-making, and learning-based methods that have expanded modern operations research's applicability.

The special issue welcomes original research presenting new optimization models, theoretical results, computational methodologies, and convergence analyses. While organized alongside ICORSI 2026 held at Indian Institute of Technology Delhi, the issue is open to all researchers in these areas regardless of conference participation.

Thèmes proposés

• Mathematical programming
• Convex, nonconvex, and mixed-integer optimization
• Stochastic, robust, and distributionally robust optimization
• Chance-constrained optimization
• Variational inequalities and equilibrium problems
• Game theory and multi-agent optimization
• Markov decision processes and stochastic games
• Reinforcement learning for sequential decision making
• Online and data-driven optimization
• Optimization under risk measures
• Bayesian optimization
• Black-box optimization