AI-DRIVEN TRAILER SPACE OPTIMIZATION: REVOLUTIONIZING LOGISTICS EFFICIENCY

Authors

  • Anoop Sagar Pradhan Utkal University, India Author

Keywords:

Trailer Space Optimization, Artificial Intelligence, Logistics Efficiency, Deep Reinforcement Learning, Supply Chain Management

Abstract

This technical article investigates the implementation and effectiveness of AI-driven systems for optimizing trailer space utilization in modern logistics operations. Through the integration of advanced technologies including Deep Reinforcement Learning, Genetic Algorithms, and Transformer-based models, the research demonstrates significant improvements in operational efficiency across major logistics providers. The comprehensive article analysis reveals substantial space utilization enhancements, notable loading times reductions, and marked improvements in order fulfillment accuracy. The implementation also yields considerable environmental benefits, including significant reductions in carbon emissions and eliminating numerous truck trips annually. The system's sophisticated architecture combines high-precision spatial mapping, real-time physics simulations, and advanced constraint management to ensure optimal load placement while maintaining safety standards. The research demonstrates how AI-driven optimization can address longstanding challenges in logistics operations while providing sustainable solutions for the future of freight transportation, offering valuable insights for industry professionals seeking to enhance their operational efficiency through technological innovation.

References

ATRI, "Critical Issues in the Trucking Industry – 2024,". https://truckingresearch.org/2024/10/critical-issue-in-the-trucking-industry-2024/

Claudia Archetti, Lorenzo Peirano, M. Grazia Speranza, "Optimization in multimodal freight transportation problems: A Survey," Volume 299, Issue 1, 16 May 2022, Pages 1-20. https://www.sciencedirect.com/science/article/pii/S0377221721006263.

Yingli Wang, Joseph Sarkis, "Emerging digitalisation technologies in freight transport and logistics: Current trends and future directions," ScienceDirect, Volume 148, April 2021, 102291. https://www.sciencedirect.com/science/article/abs/pii/S136655452100065X

Z. Soufi, P. David and Z. Yahouni, "Generation of material handling system alternatives: : A constraints satisfaction problem approach.," Volume 155, Issue C, doi.org/10.1016/j.compind.2023.10404, 2024. https://dl.acm.org/doi/10.1016/j.compind.2023.104045

Restack, "Hybrid Ai Models In Logistics Research," AI in Logistics and Distribution/Hybrid Ai Models In Logistics

Research, Oct 2024. https://www.restack.io/p/ai-in-logistics-and-distribution-answer-hybrid-ai-models-cat-ai

M.K.S. Sastry, Larry Seekumar, "Real-time Action Management in Automated Loading," July 2012, Journal of Engineering Design and Technology 10(2), DOI:10.1108/17260531211241194. https://www.researchgate.net/publication/262894229_Automation_of_real_time_monitoring_and_controlling_of_a_marine_loading_arm

O. Afolabi, "AI-Driven Logistics: Enhancing Supply Chain Efficiency with Generative AI Models," Oct. 2024. [Online]. Available: https://www.researchgate.net/publication/384681792_AI-Driven_Logistics_Enhancing_Supply_Chain_Efficiency_with_Generative_AI_Models

M. A. Khan, H. Khan, M. F. Omer, I. Ullah and M. Yasir, "Impact of Artificial Intelligence on the Global Economy and Technology Advancements," pp. 147–180, Aug. 2024. [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-97-3222-7_7

Dinuka Dulanjana, "Distributed System Architectures and Architectural Styles,", Nov. 2023. https://medium.com/@mdinukadulanjana/distributed-system-architectures-and-architectural-styles-cc3a45c202a4

Published

2024-11-18

How to Cite

Anoop Sagar Pradhan. (2024). AI-DRIVEN TRAILER SPACE OPTIMIZATION: REVOLUTIONIZING LOGISTICS EFFICIENCY. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 7(2), 1335-1345. https://ijrcait.com/index.php/home/article/view/IJRCAIT_07_02_103