A GPU-Accelerated Approach to Static Stability Assessments for Pallet Loading in Air Cargo

Date
2022-01-04
Authors
Mazur, Philipp Gabriel
Lee, No-San
Schoder, Detlef
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Abstract
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 how-to 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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Intelligent Decision Support for Logistics and Supply Chain Management, air cargo transportation, gpgpu, pallet loading, physics engine, simulation
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