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Palletizing in the air cargo sector faces a large number of constraints (e.g., aviation safety regulations) and represents a highly complex problem. In air cargo operations, there is hardly any digital support to optimize the palletizing process. As a result, desired objectives (e.g., optimal utilization of the possible loading weight, maximum use of the available loading space, or both) are often only met by chance. The goal of this research is to report on the design and performance of an intelligent decision support system that we built for the air cargo context. This system supports the manual palletizing process by considering far more constraints as well as more complex item shapes and unit load devices than any other system we know. We explain the problem context, including the essential requirements; model the solution design; and develop the intelligent decision support system as an artifact, which we then evaluate.

The static stability constraint is one of the most important constraints in pallet loading and plays a substantial role when assembling safe and loadable palletizing layouts. Current approaches reach their limits as soon as additional complexity is added, which is a given in the practice of air cargo logistics, or when performance becomes important. As our central objective, we explore a new approach to calculate static stability more performantly and to cover more complexity by relaxing several simplifying assumptions. The approach is implemented in a prototype and builds on the emerging technology of graphical processing unit acceleration in combination with physics engines. We propose a new artifact design and summarize the howto knowledge in the form of abstracted design principles. Our results demonstrate an improvement in terms of performance depending on the underlying hardware. We develop a conceptual model to assist future research in choosing a solution technology.

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