Physical question, virtual answer: Optimized real-time physical simulations and physics-informed learning approaches for cargo loading stability
Philipp Gabriel Mazur,
Johannes Werner Melsbach and
Detlef Schoder
Operations Research Perspectives, 2025, vol. 14, issue C
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.
Keywords: Static stability; Loading stability; Physical simulation; Physics-informed learning; Pallet loading problem (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:oprepe:v:14:y:2025:i:c:s2214716025000053
DOI: 10.1016/j.orp.2025.100329
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