Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review

• Published in: Applied sciences 2023-09, Vol.13 (17), p.9780
• Main Authors: Belter, Jakub, Hering, Marek, Weichbroth, Paweł
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Automation; Efficiency; Forecasts and trends; Inventory; Keywords; Literature reviews; Machine learning; Manufacturing; Methods; motion trajectory prediction; Order picking; Robots; Software; Supply chains; systematic literature review; Systematic review; warehouse management system; Warehouse stores; Warehouses
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13179780

Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field.