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13-01-2024 | Application of soft computing

Optimization of logistics flow management through big data analytics for sustainable development and environmental cycles

Author: Xin Li

Published in: Soft Computing | Issue 3/2024

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Abstract

In today’s global landscape, efficient logistics management is crucial for fostering sustainable development across industries. Integrating big data analytics into logistics operations has emerged as a transformative approach to boost efficiency, minimize waste, and mitigate environmental impact. This paper aims to explore the significance of utilizing big data analytics to optimize logistics management for sustainable development. The current challenges in logistics management primarily revolve around four key aspects: the efficiency of goods delivery, raw material costs, one-stop solutions, and service evaluation. These challenges persist in traditional logistics management due to their strong independence. Therefore, this paper is devoted to the optimization of logistics flow management through the lens of sustainable development and ecological cycles. Initially, by considering the Sustainable Development logistics management mode and survey data from 302 logistics enterprises, improvements are made in the four aspects. Subsequently, a four-element analysis function is designed, comprising factors for efficiency improvement, cost reduction, comprehensive optimization, and service enhancement. This function, which aims to assess logistics operations thoroughly, is a crucial component of the research. It includes components for enhancing services, cutting costs, improving efficiency, and conducting comprehensive optimization. MATLAB 21.0 b software is utilized for model analysis, primarily focusing on Volar model area analysis. The aim is to identify and categorize new optimization directions for SD logistics management mode based on the collected data and analyzed samples. The effectiveness of the SD logistics management mode is verified by observing model results and trends. Ultimately, the paper demonstrates the relevance of optimizing logistics management mode by significantly improving the efficiency of goods delivery, reducing raw material costs, enhancing one-stop solutions, and evaluating service-sharing information.

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Title: Optimization of logistics flow management through big data analytics for sustainable development and environmental cycles
Author: Xin Li
Publication date: 13-01-2024
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 3/2024
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-023-09591-x

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