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Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach

arXiv
Authors: Sebastián V. Romero, Eneko Osaba, Esther Villar-Rodriguez, Antón Asla

Year

2023

Paper ID

56061

Status

Preprint

Abstract Read

~2 min

Abstract Words

141

Citations

N/A

Abstract

The Bin Packing Problem is a classic problem with wide industrial applicability. In fact, the efficient packing of items into bins is one of the toughest challenges in many logistic corporations and is a critical issue for reducing storage costs or improving vehicle space allocation. In this work, we resort to our previously published quantum-classical framework known as Q4RealBPP, and elaborate on the solving of real-world oriented instances of the Bin Packing Problem. With this purpose, this paper gravitates on the following characteristics: i) the existence of heterogeneous bins, ii) the extension of the framework to solve not only three-dimensional, but also one- and two-dimensional instances of the problem, iii) requirements for item-bin associations, and iv) delivery priorities. All these features have been tested in this paper, as well as the ability of Q4RealBPP to solve real-world oriented instances.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • The Bin Packing Problem is a classic problem with wide industrial applicability.

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