Aim and Scope
Combinatorial Optimization publishes research that converts structural and mathematical insight into powerful methods for complex discrete decision problems. The journal seeks to bridge foundational theory, algorithm design, and computational practice, with particular emphasis on advances that remain effective under scale, uncertainty, dynamics, and real-world constraints.
Core areas include structure-exploiting exact and approximation algorithms; polyhedral and mathematical programming methods; hybrid optimization frameworks; learning-augmented and data-driven optimization; online, stochastic, and robust combinatorial optimization; and reliable, reproducible computational methods. The journal welcomes transformative applications in networks, logistics, scheduling, energy, healthcare, computing, artificial intelligence, and other emerging domains when they reveal broadly transferable optimization principles.
Priority is given to work offering major theoretical insight, clear algorithmic innovation, rigorous performance guarantees, or compelling computational evidence. Routine applications, minor variants of established methods, and empirical studies lacking generalizable methodological insight fall outside the journal’s primary scope.
