Warehouse optimization stands as a critical component for enhancing operational efficiency within the industrial sector. By strategically streamlining warehouse operations, organizations can achieve significant reductions in logistical costs such as the necessary footprint or traveled path, and markedly improve overall workflow efficiency including retrieval times or storage time. Despite the availability of numerous algorithms designed to identify optimal solutions for such optimization challenges, certain scenarios demand computational resources that exceed the capacities of conventional computing systems. In this context, we adapt a formulation of a warehouse optimization problem specifically tailored as a binary optimization problem and implement it in a trapped-ion quantum computer.
@article{arxiv.2411.17575,
title = {Warehouse optimization using a trapped-ion quantum processor},
author = {Alexandre C. Ricardo and Gabriel P. L. M. Fernandes and Amanda G. Valério and Tiago de S. Farias and Matheus da S. Fonseca and Nicolás A. C. Carpio and Paulo C. C. Bezerra and Christine Maier and Juris Ulmanis and Thomas Monz and Celso J. Villas-Boas},
journal= {arXiv preprint arXiv:2411.17575},
year = {2024}
}