Vol. 2 No. 2 (2022): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI-Driven Solutions for Vehicle Fleet Management

Dr. Beatriz Hernandez-Gomez
Professor of Industrial Engineering, Monterrey Institute of Technology and Higher Education (ITESM), Mexico
Cover

Published 07-11-2022

Keywords

  • Vehicle,
  • Fleet Management

How to Cite

[1]
D. B. Hernandez-Gomez, “AI-Driven Solutions for Vehicle Fleet Management”, African J. of Artificial Int. and Sust. Dev., vol. 2, no. 2, pp. 269–281, Nov. 2022, Accessed: Nov. 14, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/187

Abstract

Introduction Vehicle Fleet Management involves an effective combination of vehicles, information, and technology to help stakeholders in optimizing their logistical and transport activities. It plays a significant role in helping supply chain and logistics players shorten their time-to-market opportunities and provides a tool to keep track of all movements in the transportation process. An effective fleet management system includes elements such as vehicle procurement, vehicle deployment, vehicle monitoring, and eventually vehicle maintenance and disposal. Fleet management is now optimized with the infusion of technology to ensure logistics and transportation costs are significantly decreased, as it uses technology solutions to facilitate efficient and effective fleet management, vehicle routing, vehicle monitoring, and vehicle control solutions. Fleet management has a significant impact on overall customer service and supply chain effectiveness. It is a key enabler for value-added distribution channel activities to respond to increasingly unpredictable market conditions, as the movement of goods or people represents the most expensive form of transport in any part of the world. In fleet management, there are different families of stakeholders who are key in its running and continuity. They do have a stake in the results of processes, ensuring that the goods or people get to their destination. The emerging global supply chain has become highly competitive, and there are increasing demands for integrated supply chains. Such competition has led to a change of mode for prospective and existing fleet managers, as there is a need for them to continually upgrade themselves with the latest happenings to keep in line with recent developments. The migration of the supply systems from single distributions to sophisticated supply chains has made things really tough for transportation and logistics stakeholders who now manage fleets. The rampant growth in transport and high competition excludes any chance of insensitivity, as companies and individuals are increasingly paying attention to fleet and transportation in general.

Downloads

Download data is not yet available.

References

  1. Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
  2. Singh, Jaswinder. "The Ethics of Data Ownership in Autonomous Driving: Navigating Legal, Privacy, and Decision-Making Challenges in a Fully Automated Transport System." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 324-366.
  3. Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
  4. S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
  5. Tamanampudi, Venkata Mohit. "NLP-Powered ChatOps: Automating DevOps Collaboration Using Natural Language Processing for Real-Time Incident Resolution." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 530-567.