Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/325806 
Year of Publication: 
2025
Citation: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 14 [Article No.:] 100329 [Year:] 2025 [Pages:] 1-11
Publisher: 
Elsevier, Amsterdam
Abstract: 
Cargo stability is a crucial requirement for safe cargo loading and transport. Current state-of-the-art approaches simplify cargo loading to an idealized static problem and employ geometric- and force-based approaches. In this research, we model cargo loading stability as a dynamic problem and propose two approaches. We use (a) a physical simulation using a real-time physics engine fitted for cargo loading and (b) a physics-informed learning model trained on cargo loading data. Both approaches are capable of handling dynamic physical behavior, either explicitly through simulation, or implicitly through training a recurrent neural network on physically-biased sequential cargo loading data. Given our two objectives of maximal accuracy and minimal runtime, our benchmarking results show that our approaches can outperform current state-of-the-art static stability methods in terms of accuracy depending on the complexity scenario, but consume more runtime.
Subjects: 
Loading stability
Pallet loading problem
Physical simulation
Physics-informed learning
Static stability
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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