Optimization Methods

IOLab designs and studies algorithms for complex combinatorial optimization problems. Our core expertise includes local-search metaheuristics such as tabu search, simulated annealing, and large neighborhood search, together with multi-neighborhood and hybrid approaches.

We combine these techniques with constraint programming, mixed-integer programming, and automated algorithm configuration. The goal is not only to obtain high-quality solutions, but also to build methods that are robust, reusable, and suitable for the operational constraints of real applications.

A complementary research line studies how optimization algorithms behave. We use statistical experimental design, instance-space analysis, local-optima networks, and algorithm selection to characterize problem instances and understand which methods work best under different conditions.

These methods support the group’s application work in scheduling and timetabling, healthcare, logistics, industrial production, and decision-support systems.