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

Author

Listed:
  • Manuel Woschank

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

  • Erwin Rauch

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

  • Helmut Zsifkovits

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

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.

Suggested Citation

  • Manuel Woschank & Erwin Rauch & Helmut Zsifkovits, 2020. "A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics," Sustainability, MDPI, vol. 12(9), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3760-:d:354392
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    References listed on IDEAS

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    1. Raffaele Cioffi & Marta Travaglioni & Giuseppina Piscitelli & Antonella Petrillo & Fabio De Felice, 2020. "Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions," Sustainability, MDPI, vol. 12(2), pages 1-26, January.
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    4. Luan Thanh Le & Gunwoo Lee & Keun-Sik Park & Hwayoung Kim, 2020. "Neural network-based fuel consumption estimation for container ships in Korea," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 615-632, July.
    5. Nader Karballaeezadeh & Farah Zaremotekhases & Shahaboddin Shamshirband & Amir Mosavi & Narjes Nabipour & Peter Csiba & Annamária R. Várkonyi-Kóczy, 2020. "Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems," Energies, MDPI, vol. 13(7), pages 1-22, April.
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    Cited by:

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    2. Meir Russ, 2021. "Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models," Sustainability, MDPI, vol. 13(6), pages 1-32, March.
    3. Ahmed Zainul Abideen & Jaafar Pyeman & Veera Pandiyan Kaliani Sundram & Ming-Lang Tseng & Shahryar Sorooshian, 2021. "Leveraging Capabilities of Technology into a Circular Supply Chain to Build Circular Business Models: A State-of-the-Art Systematic Review," Sustainability, MDPI, vol. 13(16), pages 1-26, August.
    4. Mustafa Qahtan Alsudani & Mustafa Musa Jaber & Mohammed Hasan Ali & Sura Khalil Abd & Ahmed Alkhayyat & Z. H. Kareem & Ahmed Rashid Mohhan, 2023. "RETRACTED ARTICLE: Smart logistics with IoT-based enterprise management system using global manufacturing," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-31, March.
    5. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).
    6. Farajpour, Farnoush & Hassanzadeh, Alireza & Elahi, Shaban & Ghazanfari, Mehdi, 2022. "Digital supply chain blueprint via a systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

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