Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/79536

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

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Title:A GPU-Accelerated Approach to Static Stability Assessments for Pallet Loading in Air Cargo
Authors:Mazur, Philipp Gabriel
Lee, No-San
Schoder, Detlef
Keywords:Intelligent Decision Support for Logistics and Supply Chain Management
air cargo transportation
gpgpu
pallet loading
physics engine
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Date Issued:04 Jan 2022
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/79536
ISBN:978-0-9981331-5-7
DOI:10.24251/HICSS.2022.204
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Intelligent Decision Support for Logistics and Supply Chain Management


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