Abstract
Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field of artificial intelligence, thanks to the wide interest from industry and logistics. Since decades, many variants have been proposed, with the three-dimensional Bin Packing Problem as the closest one to real-world use cases. We introduce a hybrid quantum-classical framework for solving real-world three-dimensional Bin Packing Problems (Q4RealBPP), considering different realistic characteristics, such as: (1) package and bin dimensions, (2) overweight restrictions, (3) affinities among item categories and (4) preferences for item ordering. Q4RealBPP permits the solving of real-world oriented instances of 3 dBPP, contemplating restrictions well appreciated by industrial and logistics sectors.
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