Data-Driven and Sustainable Transportation Route Optimization in Green Logistics Supply Chain

Qizhen Chen

doi:10.62177/apemr.v1i6.118

Authors

Qizhen Chen Amazon

DOI:

https://doi.org/10.62177/apemr.v1i6.118

Keywords:

Green Logistics Supply Chain, Transportation Route Optimization, Data-Driven, Sustainable Development

Abstract

In the green logistics supply chain, transportation route optimization faces urgent problems such as high energy consumption and environmental pollution. This paper aims to achieve sustainable optimization of transportation routes through a data-driven approach. First, a large amount of transportation-related data is collected, including vehicle operation information, cargo characteristics, road conditions and meteorological factors. Big data analysis technology is used to clean and extract features from the data, and a transportation demand forecasting model is constructed. Then, a new optimization model is designed using the particle swarm optimization algorithm, with the goal of minimizing transportation costs and carbon dioxide emissions. In actual application cases, by optimizing the transportation routes of a logistics company, the results showed that the lowest transportation cost was 1,512 dollars and the lowest carbon dioxide emissions were 1.2 tons. Data-driven transportation route optimization not only improves logistics efficiency, but also promotes environmental sustainable development.

Downloads

Download data is not yet available.

References

Sun Lin. Optimal ship transport route planning based on swarm intelligence optimization algorithm[J]. Ship Science and Technology, 2024, 46(12): 166-169.

Liu Min, Gu Qinghua, Wang Qian. Research on optimization of open-pit mine oil-electric hybrid truck transportation with optimized comprehensive cost[J]. Mining Technology, 2024, 24(5): 286-292.

Zhang Yanli, Han Lei. Optimization and implementation of rice supply chain in my country[J]. Northern Rice, 2024, 54(4): 52-54.

Liu Dalong. Research on optimization of fresh agricultural product supply chain under digital background[J]. China Management Informationization, 2024, 27(9): 80-83.

Yu Guoqing, Yang Caijuan, Chen Yushi, Zhang Bing. Application of supply chain optimization model in optimization of synthetic resin supply chain[J]. Petrochemical Technology and Economy, 2024, 40(1): 10-15.

Putha S. AI-Driven Predictive Analytics for Supply Chain Optimization in the Automotive Industry[J]. Journal of Science & Technology, 2022, 3(1): 39-80.

Sahu M K. AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management[J]. Journal of Science & Technology, 2020, 1(1): 424-464.

Baloch N, Rashid A. Supply chain networks, complexity, and optimization in developing economies: A systematic literature review and meta-analysis: Supply chain networks and complexity: A meta-analysis[J]. South Asian Journal of Operations and Logistics, 2022, 1(1): 14-19. DOI: https://doi.org/10.57044/SAJOL.2022.1.1.2202

Ikevuje A H, Anaba D C, Iheanyichukwu U T. Optimizing supply chain operations using IoT devices and data analytics for improved efficiency[J]. Magna Scientia Advanced Research and Reviews, 2024, 11(2): 070-079. DOI: https://doi.org/10.30574/msarr.2024.11.2.0107

Reddy V M, Nalla L N. Optimizing E-Commerce Supply Chains Through Predictive Big Data Analytics: A Path to Agility and Efficiency[J]. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 2024, 15(1): 555-585.

Al-Awamleh H, Alhalalmeh M, Alatyat Z, et al. The effect of green supply chain on sustainability: Evidence from the pharmaceutical industry[J]. Uncertain Supply Chain Management, 2022, 10(4): 1261-1270. DOI: https://doi.org/10.5267/j.uscm.2022.8.002

Nahr J G, Nozari H, Sadeghi M E. Green supply chain based on artificial intelligence of things (AIoT)[J]. International Journal of Innovation in Management, Economics and Social Sciences, 2021, 1(2): 56-63. DOI: https://doi.org/10.52547/ijimes.1.2.56

Khan M, Ajmal M M, Jabeen F, et al. Green supply chain management in manufacturing firms: A resource‐based viewpoint[J]. Business Strategy and the Environment, 2023, 32(4): 1603-1618. DOI: https://doi.org/10.1002/bse.3207

Nureen N, Sun H, Irfan M, et al. Digital transformation: fresh insights to implement green supply chain management, eco-technological innovation, and collaborative capability in manufacturing sector of an emerging economy[J]. Environmental Science and Pollution Research, 2023, 30(32): 78168-78181. DOI: https://doi.org/10.1007/s11356-023-27796-3

Heydari J, Govindan K, Basiri Z. Balancing price and green quality in presence of consumer environmental awareness: A green supply chain coordination approach[J]. International Journal of Production Research, 2021, 59(7): 1957-1975. DOI: https://doi.org/10.1080/00207543.2020.1771457