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Description
Proposing air cargo palletizing solutions requires an assessment by a physics engine of whether a solution is physically stable, which can take up a disproportionate amount of computation and, thus, produce a bottleneck in the optimization pipeline. This problem can be tackled by replacing the physics engine with a data-driven model that learns to map proposed packing pattern solutions directly to its stability value. We develop a prototype of such a datadriven model and find that this approach yields practicable results and does so multiple orders of magnitudes faster than a commonly used physics engine.
Recommended Citation
Mazur, Philipp Gabriel; Lee, No-San; Euskirchen, Janik; and Schoder, Detlef, "Predicting Static Stability with Data-Driven Physics in Air Cargo Palletizing" (2022). Wirtschaftsinformatik 2022 Proceedings. 3.
https://aisel.aisnet.org/wi2022/business_analytics/business_analytics/3
Predicting Static Stability with Data-Driven Physics in Air Cargo Palletizing
Proposing air cargo palletizing solutions requires an assessment by a physics engine of whether a solution is physically stable, which can take up a disproportionate amount of computation and, thus, produce a bottleneck in the optimization pipeline. This problem can be tackled by replacing the physics engine with a data-driven model that learns to map proposed packing pattern solutions directly to its stability value. We develop a prototype of such a datadriven model and find that this approach yields practicable results and does so multiple orders of magnitudes faster than a commonly used physics engine.