Tuning the Parameters of a Loading Algorithm

ORTEC is one of the largest global providers of software solutions for complex decision-making problems. The newly developed software tool ORTEC Pallet and Load Building (OPLB) aims to facilitate container loading processes for customers worldwide. It contains a heavily parameterized algorithmic framework of which the performance on customer-specific problem instances depends on the quality of its parameter configuration. These parameters form the subject of this research. We propose uRace, an innovative unified Race algorithm that ensures efficient offline parameter tuning of deterministic, categorically parameterized algorithms. Using uRace to tune the parameters of OPLB, we outperform known constructive heuristics in the literature on both an academic and a practical problem set, while showing competitive results on academic benchmark problems compared to state-of-the-art loading algorithms.