Intelligent warehouse space optimization using convolutional neural networks
1 Department of Computer Science and Engineering, Indian Institute of Technology Jammu, Jammu and Kashmir (UT), India.
2 Continuing Education Programme, Indian Institute of Technology Patna, Bihar, India.
Review
World Journal of Advanced Engineering Technology and Sciences, 2023, 10(02), 030–036.
Article DOI: 10.30574/wjaets.2023.10.2.0278
Publication history:
Received on 12 September 2023; revised on 31 October 2023; accepted on 03 November 2023
Abstract:
In the era of rapid digital transformation, global commerce and its intricately woven supply chains face unprecedented demands and challenges. As businesses strive to meet the ever-expanding consumer expectations, the spotlight is increasingly on warehouses - the vital nodes of these supply chains. Warehousing, once considered a mere storage facility, is now at the epicenter of a logistical revolution, with the quest for optimized space and streamlined operations becoming paramount. Amidst this evolving paradigm, the present research delves deep into the capabilities of Convolutional Neural Networks (CNNs), an advanced machine learning construct, examining its potential to redefine warehousing strategies. Delving into the intricate architecture of CNNs, we explore their robustness in analyzing and interpreting spatial relationships, a skillset inherently suited for space optimization tasks. This paper aims not just to present the technological prowess of CNNs, but to showcase how their integration could mark a paradigm shift in the warehousing domain, paving the way for smarter, more efficient storage solutions in an increasingly digital world.
Keywords:
Convolutional Neural Networks; Warehouse Management; Space Efficiency; Deep Learning; Spatial Analysis.
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0