Citation:

Sai C. Pallaprolu , 2022. "Dynamic Route Optimization for Trucks: Survey of Machine Learning-Based Techniques for Efficient Logistics and Transportation ", ESP Journal of Engineering & Technology Advancements, 2(4): 192-201.

Abstract:

In the contemporary logistics and transportation infrastructure, dynamic optimization of truck routes is the key to achieving the highest efficiency, the reduction of costs, and ensuring timely delivery. Dynamic Vehicle Routing Problem (DVRP) reflects real-life uncertainties such as stochastic travel time, dynamic customer demands and changing availability of vehicles. In this paper, a detailed literature review of machine learning-based methods of dynamic route optimization in truck transportation systems. It surveys the unification of dissimilar data streams such as GPS positions, road surveillance systems, meteorological applications and Fleet management applications and highlights the significance of data preprocessing in a dependable model execution. Classical machine learning models like Support Vector Machines and Logistic Regression are presented and also advanced deep learning models like Long Short-Term Memory and Gated Recurrent Unit networks are discussed that are effective in capturing intricate spatiotemporal traffic behavior. The adaptive real-time decision-making is also discussed through reinforcement learning techniques namely Deep Q-Networks and policy-gradient approaches. According to the comparison performance results reported in recent works, deep learning-based routing frameworks can minimize average delivery time by more than 20%, increase on-time delivery rates by an average of 17% points, and reduce fuel consumption by up to 13% in comparison with traditional DVRP approaches. The survey also ends with the identification of the existing challenges and future research areas in terms of intelligent, scalable, and sustainable truck route optimization systems. Such results show how the ML-based approaches can be used to facilitate smart, scalable, and sustainable optimization of truck routes.

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Keywords:

Dynamic Vehicle Routing, Trucking Logistics, Machine Learning (ML), Route Optimization, Autonomous Trucks, Big Data Analytics, Transportation Efficiency.