Cite this DOI
10.46243/jst.2023.v8.i12.pp230-255 · HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDLY LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION
APA (7th edition)
Venkat Garikipati, Charles Ubagaram, Narsing Rao Dyavani, Bhagath Singh Jayaprakasam, & Hemnath_R (2023). HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDLY LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION. *Journal of Science & Technology*, *08*(12), 230–255. https://doi.org/10.46243/jst.2023.v8.i12.pp230-255
⬇ text Italics are shown as *asterisks* in plain text — the journal or book title and the volume.
BibTeX
@article{venkatgarikipati2023hybrid,
author = {Venkat Garikipati and Charles Ubagaram and Narsing Rao Dyavani and Bhagath Singh Jayaprakasam and Hemnath\_R},
title = {{HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDLY LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION}},
journal = {Journal of Science \& Technology},
year = {2023},
month = {dec},
volume = {08},
number = {12},
pages = {230--255},
publisher = {Longman Publishers},
issn = {2456-5660},
doi = {10.46243/jst.2023.v8.i12.pp230-255},
url = {https://doi.org/10.46243/jst.2023.v8.i12.pp230-255},
language = {en},
abstract = {The increasing need for climate change mitigation has led the way towards making logistics and supply chain management greener. While global trade and transportation are growing, the pollution factor, such as carbon footprints and the use of energy, has taken center stage as a matter of concern. With this, Hybrid AI Models and Sustainable Machine Learning are being incorporated into green logistics for reducing carbon emissions and creating sustainable green supply chains. These AI-based solutions employ techniques such as deep learning, optimization algorithms, and neural networks to optimize route transportation, improve vehicle performance, and optimize resource allocation. With the use of these technologies, carbon footprint minimization has been able to register improvements of up to 30\% in certain situations, and improvements in resource efficiency have been witnessed at 25\%. The convergence of these technologies not only enables the reduction of carbon footprints but also increases sustainability in supply chain management. The ability of Hybrid AI models to spur sustainability is discussed in this paper through more efficient logistics functions, maximizing resource utilization, and enabling green supply chain practices while creating a mechanism to attain long-term environmental objectives.}
}RIS (EndNote, Zotero, Mendeley)
TY - JOUR TI - HYBRID AI MODELS AND SUSTAINABLE MACHINE LEARNING FOR ECO-FRIENDLY LOGISTICS, CARBON FOOTPRINT REDUCTION, AND GREEN SUPPLY CHAIN OPTIMIZATION AU - Venkat Garikipati AU - Charles Ubagaram AU - Narsing Rao Dyavani AU - Bhagath Singh Jayaprakasam AU - Hemnath_R JO - Journal of Science & Technology PY - 2023 DA - 2023/12/29/ VL - 08 IS - 12 SP - 230 EP - 255 PB - Longman Publishers SN - 2456-5660 LA - en AB - The increasing need for climate change mitigation has led the way towards making logistics and supply chain management greener. While global trade and transportation are growing, the pollution factor, such as carbon footprints and the use of energy, has taken center stage as a matter of concern. With this, Hybrid AI Models and Sustainable Machine Learning are being incorporated into green logistics for reducing carbon emissions and creating sustainable green supply chains. These AI-based solutions employ techniques such as deep learning, optimization algorithms, and neural networks to optimize route transportation, improve vehicle performance, and optimize resource allocation. With the use of these technologies, carbon footprint minimization has been able to register improvements of up to 30% in certain situations, and improvements in resource efficiency have been witnessed at 25%. The convergence of these technologies not only enables the reduction of carbon footprints but also increases sustainability in supply chain management. The ability of Hybrid AI models to spur sustainability is discussed in this paper through more efficient logistics functions, maximizing resource utilization, and enabling green supply chain practices while creating a mechanism to attain long-term environmental objectives. DO - 10.46243/jst.2023.v8.i12.pp230-255 UR - https://doi.org/10.46243/jst.2023.v8.i12.pp230-255 ER -
CSL-JSON
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} ⬇ .json What citeproc and reference managers read; the DOI system hands it out for Accept: application/vnd.citationstyles.csl+json, and so does this registry's resolver.
From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i12.pp230-255 gives all four in one JSON answer.
