Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach
arXiv:2308.02787 · doi:10.1109/ITSC57777.2023.10422581
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.
arXiv comment7 pages, 7 figures, paper accepted for being presented in the upcoming 26th IEEE International Conference on Intelligent Transportation Systems - ITSC 2023
References in corpus (8)
- Quantum Computing in the NISQ era and beyond
- Quantum computing for finance: overview and prospects
- Quantum computing for energy systems optimization: Challenges and opportunities
- Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
- Democratization of Quantum Technologies
- Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers
- Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem
- Benchmark dataset and instance generator for Real-World Three-Dimensional Bin Packing Problems