Heuristics and metaheuristics are approximative methods that can be used in order to solve discrete optimization problems. These methods are useful when exact methods fail, which is often the case in industrial problems. Metaheuristics have proven to be remarkably effective in providing good solutions to very complex optimization problems in a relatively short amount of time. Heuristics and metaheuristics do not come with performance guarantees. They find good solutions, but they do not come with a guarantee on the distance to the optimal value.

The Group of Optimization at SINTEF has for more than twenty years performed research on heuristics and metaheuristics for solving real-life optimizations problems. Based on our cutting edge competence in optimization, we have delivered solutions that help our clients in optimizing their business.