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

Abstract

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.

MARLIN Method: Enhancing Warehouse Resilience in Response to Disruptions

“…The pandemic's duration may have significant long-term effects on customer purchasing channels, and merchants must adjust to this mutation, for example, by favoring a multi-channel, physical, and online marketing distribution approach [13]. In addition, these multiple challenges encountered during the pandemic underscore the limitations of traditional warehouse management methods and highlight the need for a more resilient and adaptive approach [131]. Indeed, the field of warehouse management has undergone significant changes, driven by the increasing complexity of supply chain operations.…”