The IUP Journal of Operations Management
Optimization Techniques in Logistics Management Using Deep Learning Algorithms

Article Details
Pub. Date : Aug, 2023
Product Name : The IUP Journal of Operations Management
Product Type : Article
Product Code : IJOM020823
Author Name : Sandeep Bhattacharjee
Availability : YES
Subject/Domain : Management
Download Format : PDF Format
No. of Pages : 08



Logistics management plays a crucial role in supply chain operations, encompassing various tasks such as transportation, inventory management, and order fulfillment. The application of deep learning algorithms has emerged as a promising approach to optimize logistics processes and enhance efficiency. This paper explores the utilization of deep learning algorithms for optimization in logistics management, focusing on transportation routing, warehouse management, and demand forecasting. The study discusses the key deep learning algorithms employed in these areas, including deep neural networks, convolutional neural networks, recurrent neural networks, and reinforcement learning. Furthermore, it presents case studies and empirical evidence that demonstrate the effectiveness of deep learning techniques in improving logistics operations. The paper provides an overview of logistics management and the motivation for employing deep learning algorithms for optimization.


Due to e-commerce expansion, globalization, and customer demands for faster delivery, logistics management has had to deal with growing complexity in recent years. By optimizing a number of logistical activities, deep learning algorithms provide potential answers to these problems. Heuristic algorithms and mathematical programming models were primarily used in the early optimization techniques in logistics management. These methods intended to improve a number of logistics activities, including facility location, inventory control, and transportation. A mathematical modeling method called Linear Programming (LP) is used to optimize linear objective functions with respect to linear constraints. For transportation and allocation issues, LP has been widely utilized in logistics management (Dantzig, 1951).