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Auxological Status of Modern Primary School Students of Nizhny Novgorod Region
Kalyuzhny Evgeniy Aleksandrovich, Mukhina Irina Vasilievna, Bogomolova Elena Sergeevna, Galova Elena Anatolyevna, Puzhak Svetlana Andreevna and Baklanova Ekaterina Sergeevna
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Levetiracetam-induced Rhabdomyolysis - A Rare Complication
Ayisha Farooq Khan, Naeemuddin Shaikh, Faryal Abdy and Dureshahwar Kanwar
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We aim to foster interdisciplinary discussions that contribute to fast-tracking research.

Engineering Group Editorial 記事ID: igmin220

Machine Learning Applied to Electric Vehicle Routing Problem: Optimizing Costs for a Sustainable Environment

Machine Learning Industrial Engineering DOI10.61927/igmin220 Affiliation

Affiliation

    Jalel Euchi, OLID Laboratory, Higher Institute of Industrial Management, University of Sfax, Sfax, Tunisia, Email: [email protected]

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要約

The global move towards Electric Vehicles (EVs) marks a crucial step towards sustainable transportation. However, effectively integrating EVs into the current infrastructure demands more than technological advancements. One of the key challenges is optimizing the routing of EVs to minimize costs and environmental impact. This editorial examines the role of Machine Learning (ML) in addressing the electric vehicle routing problem (ESVRP), highlighting its potential to transform cost optimization and sustainability in transportation. Routing is a fundamental part of transportation logistics, influencing efficiency, cost, and environmental impact. While traditional internal combustion engine vehicles have established routing systems, EVs present unique challenges such as limited battery capacity, longer refueling times, and fewer charging stations. These factors require advanced routing solutions that can dynamically adapt to various constraints.

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