Applications of Artificial Intelligence in Cross Docking: A Systematic Literature Review

• Published in: The Journal of computer information systems Vol. ahead-of-print; no. ahead-of-print; pp. 1 - 21
• Main Authors: Altaf, Amna, El Amraoui, Adnen, Delmotte, Francois, Lecoutre, Christophe
• Format: Journal Article
• Language: English
• Published: Stillwater Taylor & Francis 03.09.2023; Taylor & Francis Ltd
• Subjects: Artificial Intelligence; Computer Science; Cross docking; Docking; E-SALSA framework; Humanities and Social Sciences; Literature reviews; Logistics; Meta-analysis; Supply chain management; systematic literature review (SLR); Systematic review; Cross docking; Supply chain management; Artificial Intelligence; E-SALSA framework; Systematic literature review (SLR)
• ISSN: 0887-4417; 2380-2057
• DOI: 10.1080/08874417.2022.2143455

In this paper, we present the research issues, the representation models, as well as the methodological and algorithmic approaches that have been proposed for cross-docking systems in the literature. More specifically, we have conducted a systematic literature review so as to analyze the contribution of Artificial Intelligence (AI), and more generally AI-based techniques, to cross-docking systems. One immediate interest of this work is that it allows us to identify some new potential uses of AI-based techniques for solving cross-docking problems. To conduct our analysis, we have extended the standard approach of systematic literature review called SALSA; e-SALSA is the novel derived and enhanced approach we propose in our study. It consists of seven steps for carrying out a systematic literature review based on a meta-analysis of the available data on the subject of AI-based techniques applied to the domain of cross docking (AI4 CD: Artificial Intelligence for Cross-Docking). In the light of the results of our review and analysis, several new scientific issues are identified on AI4 CD, giving us the opportunity of suggesting some new directions of research.