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

被引:5
|
作者
Altaf, Amna [1 ]
El Amraoui, Adnen [1 ,3 ]
Delmotte, Francois [1 ]
Lecoutre, Christophe [2 ]
机构
[1] Univ Artois, UR 3926, Lab Genie Informat & Automat Artois LGI2A, Bethune, France
[2] Univ Artois, CRIL CNRS, UMR 8188, Lens, France
[3] Univ Artois, UR 3926, Lab Genie Informat & Automat Artois LGI2A, F-62400 Bethune, France
关键词
Cross docking; systematic literature review (SLR); E-SALSA framework; Artificial Intelligence; supply chain management; PARTICLE SWARM OPTIMIZATION; SUPPLY CHAIN MANAGEMENT; VEHICLE-ROUTING PROBLEM; SCHEDULING PROBLEM; INBOUND TRUCKS; METAHEURISTIC ALGORITHMS; GENETIC ALGORITHM; NETWORK DESIGN; MODEL; LOCATION;
D O I
10.1080/08874417.2022.2143455
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1280 / 1300
页数:21
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