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

Woschank, Manuel; Rauch, Erwin; Zsifkovits, Helmut

doi:10.3390/su12093760

Open AccessArticle

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

by

Manuel Woschank

^1,*

,

Erwin Rauch

²

and

Helmut Zsifkovits

¹

Chair of Industrial Logistics, Montanuniversitaet Leoben, 8700 Leoben, Austria

²

Industrial Engineering and Automation (IEA), Faculty of Science and Technology, Free University of Bozen-Bolzano, 39100 Bolzano, Italy

^*

Author to whom correspondence should be addressed.

Sustainability 2020, 12(9), 3760; https://doi.org/10.3390/su12093760

Submission received: 31 March 2020 / Revised: 29 April 2020 / Accepted: 29 April 2020 / Published: 6 May 2020

(This article belongs to the Special Issue Industry 4.0 for SMEs - Smart Manufacturing and Logistics for SMEs)

Download

Browse Figures

Versions Notes

Abstract

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.

Keywords: industry 4.0; artificial intelligence; machine learning; deep learning; smart logistics; logistics 4.0

Share and Cite

MDPI and ACS Style

Woschank, M.; Rauch, E.; Zsifkovits, H. A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics. Sustainability 2020, 12, 3760. https://doi.org/10.3390/su12093760

AMA Style

Woschank M, Rauch E, Zsifkovits H. A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics. Sustainability. 2020; 12(9):3760. https://doi.org/10.3390/su12093760

Chicago/Turabian Style

Woschank, Manuel, Erwin Rauch, and Helmut Zsifkovits. 2020. "A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics" Sustainability 12, no. 9: 3760. https://doi.org/10.3390/su12093760

APA Style

Woschank, M., Rauch, E., & Zsifkovits, H. (2020). A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics. Sustainability, 12(9), 3760. https://doi.org/10.3390/su12093760

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI