Machine Learning and Sustainable Logistics: Analyzing the Role of Optimization Algorithms in Supply Chain Management
DOI:
https://doi.org/10.5281/Keywords:
Machine Learning, Sustainable Logistics, Supply Chain Management, Optimization Algorithms, Sustainability, Operational Efficiency, Carbon Emissions, Genetic Algorithms, Neural Networks, Circular EconomyAbstract
In the modern business landscape, sustainability and operational efficiency are two pillars that drive competitive advantage. Supply chain management (SCM), a core aspect of logistics, is increasingly seen as pivotal in advancing sustainability initiatives while maintaining business viability. Machine learning (ML), with its wide array of optimization algorithms, offers innovative solutions to supply chain challenges, from minimizing carbon emissions to optimizing delivery routes. This paper explores the interplay between ML optimization techniques and sustainable logistics, examining how these technological advancements influence the structure and efficiency of supply chains. Through a deep dive into various optimization algorithms, including linear programming, genetic algorithms, and neural networks, the paper uncovers the mechanisms by which these techniques reduce waste, enhance energy efficiency, and foster circular economies. Furthermore, this research emphasizes the need for businesses to adopt sustainable practices, driven by the pressure of regulatory standards and consumer demand for eco-conscious products. By scrutinizing the impact of machine learning on the logistical side of the supply chain, the research showcases how businesses can leverage technology to achieve both sustainability and profitability.
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