Manuscript Topics

This SI aims to provide a forum for recent advances at the intersection of Operations Research (OR) and Artificial Intelligence (AI), with a clear mathematical and methodological focus. The issue will address the development of algorithms, models, and hybrid frameworks that combine optimization, simulation, machine learning, and generative AI techniques to solve complex decision-making problems arising in real-life systems. Particular attention will be given to mathematically grounded approaches that integrate exact methods, metaheuristics, stochastic modeling, predictive analytics, neural computing, and data-driven optimization. The rapid evolution of AI methods, including deep learning, reinforcement learning, large language models, and generative AI, is opening new opportunities for the OR community. At the same time, OR contributes rigorous modeling, optimization, uncertainty analysis, and explainability tools that can improve the reliability and efficiency of AI-driven systems. This SI seeks contributions that advance this interface from both theoretical and applied perspectives, including methodological innovations, computational studies, and application-oriented research in areas such as logistics, transportation, healthcare, energy, finance, manufacturing, smart cities, and digital services.

Topics of interest include, but are not limited to:
* Mathematical optimization and AI integration
* Hybrid optimization and machine learning models
* Sim heuristics and simulation-optimization methods
* Learn heuristics and data-driven metaheuristics
* Agile optimization under uncertainty and dynamic environments
* Neural computing for optimization and decision-making
* Generative AI for optimization modeling and analytics
* Biased-randomized algorithms and transformers
* Reinforcement learning for combinatorial optimization
* Explainable and trustworthy AI in OR applications
* Optimization for large-scale and real-time systems
* Metaheuristics combined with deep learning architectures
* Intelligent scheduling, routing, and resource allocation
* Applications of OR-AI hybrid methods in industry and services

The Special Issue welcomes original research articles, review papers, and methodological contributions that provide rigorous mathematical foundations, algorithmic developments, or high-impact applications demonstrating the value of integrating OR and AI methodologies.

Instructions for authors
https://www.aimspress.com/math/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/