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BRIEF RESEARCH REPORT article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1465186

A Novel Framework for Automated Warehouse Layout Generation

Provisionally accepted
Atefeh Shahroudnejad Atefeh Shahroudnejad 1*Payam Mousavi Payam Mousavi 1Oleksii Perepelytsia Oleksii Perepelytsia 2Sahir Sahir Sahir Sahir 1David Staszak David Staszak 1Matthew E. Taylor Matthew E. Taylor 1Brent Bawel Brent Bawel 2
  • 1 Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada
  • 2 Routeique Inc, Edmonton, Canada

The final, formatted version of the article will be published soon.

    Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layouts within given spatial parameters, adhering to all functional requirements. The feasibility of the generated layouts is verified based on criteria such as item accessibility, required minimum clearances, and aisle connectivity. A scoring function is then used to evaluate the feasible layouts considering the number of storage locations, access points, and accessibility costs. We demonstrate our method's ability to produce feasible, optimal layouts for a variety of warehouse dimensions and shapes, diverse door placements, and interconnections. This approach, currently being prepared for deployment, will enable human designers to rapidly explore and confirm options, facilitating the selection of the most appropriate layout for their use-case.

    Keywords: AI, constrained optimization, Automation, Warehouse design, Logistics

    Received: 15 Jul 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Shahroudnejad, Mousavi, Perepelytsia, Sahir, Staszak, Taylor and Bawel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Atefeh Shahroudnejad, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.