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

doi: 10.3390/su12093760
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.
- National Institute for Nuclear Physics Italy
- University of Leoben Austria
- Free University of Bozen-Bolzano Italy
- University of Leoben Austria
Environmental effects of industries and plants, Renewable Energy, Sustainability and the Environment, Geography, Planning and Development, deep learning, TJ807-830, smart logistics, Management, Monitoring, Policy and Law, artificial intelligence, TD194-195, Renewable energy sources, Environmental sciences, machine learning, logistics 4.0, GE1-350, industry 4.0
Environmental effects of industries and plants, Renewable Energy, Sustainability and the Environment, Geography, Planning and Development, deep learning, TJ807-830, smart logistics, Management, Monitoring, Policy and Law, artificial intelligence, TD194-195, Renewable energy sources, Environmental sciences, machine learning, logistics 4.0, GE1-350, industry 4.0
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).193 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1% visibility views 1 - 1views
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