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

arXiv: https://arxiv.org/abs/2308.02787 · HTML: https://mlsanity.com/paper/2308.02787/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method](https://mlsanity.com/paper/1708.05930/) (2017)
- [Quantum computing for energy systems optimization: Challenges and opportunities](https://mlsanity.com/paper/2003.00254/) (2020)
- [Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers](https://mlsanity.com/paper/2303.01977/) (2023)
- [Benchmark dataset and instance generator for Real-World Three-Dimensional Bin Packing Problems](https://mlsanity.com/paper/2304.14712/) (2023)

## Built on by (most cited first, 2 of 2)

- [D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis](https://mlsanity.com/paper/2410.07980/) (2024)
- [Introducing Resizable Region Packing Problem in Image Generation, with a Heuristic Solution](https://mlsanity.com/paper/2509.16363/) (2025)

## Abstract (arXiv)

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.