Interpretability, Adaptability and Scalability of Variable Neighborhood Search

Variable Neighborhood Search (VNS) has reached its 25th anniversary as an effective and accessible metaheuristic for combinatorial optimization. This paper explores how VNS’s ingenious method to escape local optima exhibits the properties of interpretability, adaptability and scalability, making it well-suited for tackling large and complex real-world problems. We first outline how the simple, modular design of VNS lends itself to insightful problem analysis and systematic formulation of the search space. We then discuss how VNS organically integrates with other methods as a hybrid and readily leverages parallelization and AI/ML capabilities for scalability. Finally, we propose recommendations to further advance VNS through establishing public code repositories and problem libraries, documenting challenges and successes with real-world implementations, actively engaging across metaheuristics, and popularizing VNS as an accessible optimization technique.