Quantum-CLP: Quantum computing optimization for container loading problems: a new frontier in logistics optimization

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT) (Concurso para Projetos de Investigação de caráter Exploratório em Todos os Domínios Científicos 2024), grant 2024.15096.PEX. Funded amount: EUR 59,782.40, as stated by the source. Runs 2026-02-23 to 2027-08-22. Keywords: Quantum Computing, Cutting and Packing Problems, Three-dimensional Packing Problems, Combinatorial optimization, Social sciences. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2026-06-24); national funding data, not CORDIS. Abstract given by the source: The primary goal of this project is to leverage the potential of quantum computing (QC) to develop an efficient approach for solving three-dimensional container loading problems (3D-CLPs). QC is revolutionizing computational efficiency by offering groundbreaking capabilities to find optimal solutions to complex combinatorial optimization problems that are often difficult for classical methods. Unlike traditional algorithms, which explore solutions sequentially, quantum algorithms utilize quantum mechanical principles, such as superposition and entanglement, to dramatically reduce the number of steps required to evaluate multiple solutions, opening new frontiers in optimization. While QC alone has yet to demonstrate this capability on large-scale problems, hybrid approaches that combine QC with classical computing have shown significant potential for tackling real-world problems. This paradigm shift is particularly impactful for complex problems like the 3D-CLPs, where achieving high-quality solutions within reasonable computational time remains a major challenge. The 3D-CLP is a fundamental problem in logistics and transportation, focusing on optimizing the space usage inside transport units while satisfying operational constraints such as weight distribution and cargo stability. The efficient resolution of 3D-CLPs is both commercially and socially impactful, as it reduces logistics costs by minimizing the number of used containers and lowers transport-related carbon emissions. Despite significant progress in operations research and computational optimization, classical methods struggle to find optimal solutions within reasonable timeframes, particularly for large-scale, real-world instances. Moreover, since considering relevant packing requirements (i.e., constraints) introduces additional computational complexity, many studies neglect or oversimplify these requirements to maintain tractability, limiting their real-world applicability. Given these limitations and [shortened; the full text is on the OpenAIRE page]

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Fundação para a Ciência e a Tecnologia, I.P. (FCT). Retrieved 2026-10-22. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: The project "Quantum-CLP" aims to use quantum computing to optimize three-dimensional container loading problems, funded by FCT with a grant of EUR 59,782.40 from 2024-02-23 to 2027-08-22. It focuses on combinatorial optimization in logistics. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.