Deep Learning-Based Route Optimization and Demand Forecasting for Smart Logistics

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 50-58, March 2026.

https://doi.org/10.58482/ijersem.v2i3.7

This work is licensed under a Creative Commons Attribution 4.0 International License.

Deep Learning-Based Route Optimization and Demand Forecasting for Smart Logistics

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B. Jhansi

G Adarsh

C Lakshmi Devi

Chevvu Hemanth Prasad Reddy

Sripathi Hemanth

Kancharla Kishore

Department of CSE, Siddartha Institute of Science and Technology, Puttur, India

Abstract: The efficient management of transportation and logistics is an essential part of smart supply-chain management systems. The proposed project aims to design and implement an efficient software-based solution for dynamic delivery route optimization and product demand forecasting using Deep Learning techniques, without requiring any hardware components, such as Raspberry Pi, sensors, and the like. The proposed software uses past delivery routes, geographical locations, and Deep Learning algorithms such as LSTM to forecast demand, Google OR-Tools for dynamic route optimization, and aims to minimize delivery times. The major innovations in the proposed software include real-time dynamic route optimization, AI-powered delay prediction, and route optimization for carbon-emission calculations. The software has been fully developed using the Python programming language and its libraries, such as TensorFlow, Scikit-learn, Pandas, and Google OR-Tools, and can be run on any PC-based environment, thereby incorporating real-world scenarios for the TSP, Demand Clustering, and Traffic Flow predictions.

Keywords: Smart Logistics, Route Optimization, Demand Forecasting, Long Short-Term Memory, Supply Chain Analytics.

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This article is part of Volume 2, Issue 3 (March 2026)