A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

被引:95
|
作者
Woschank, Manuel [1 ]
Rauch, Erwin [2 ]
Zsifkovits, Helmut [1 ]
机构
[1] Univ Leoben, Chair Ind Logist, A-8700 Leoben, Austria
[2] Free Univ Bozen Bolzano, Fac Sci & Technol, Ind Engn & Automat IEA, I-39100 Bolzano, Italy
基金
欧盟地平线“2020”;
关键词
industry; 4; 0; artificial intelligence; machine learning; deep learning; smart logistics; logistics; MANAGEMENT; ALGORITHMS; MODELS; SYSTEM;
D O I
10.3390/su12093760
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.
引用
收藏
页数:23
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