Machine learning-based route optimization for enhancing logistics support in remote mining operations
1 Department of Petroleum and Mining Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh.
2 Department of Information and Communication Technology, SunUp International School and College, Kushtia, Bangladesh.
3 Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati, Bangladesh.
4 Department of CSE, Green University of Bangladesh, Dhaka, Bangladesh.
5 Institute of Energy, University of Dhaka, Dhaka, Bangladesh.
6 Department of Statistics and Data Science, Jahangirnagar University, Dhaka, Bangladesh.
International Journal of Frontiers in Engineering and Technology Research, 2025, 08(02), 063-071.
Article DOI: 10.53294/ijfetr.2025.8.2.0038
Publication history:
Received on 07 May 2025; revised on 25 June 2025; accepted on 27 June 2025
Abstract:
Logistics is considered as the corner stone of storage efficiency and route optimization
(Boujarra et al., 2024)
. The optimization techniques in the realm of logistics have greatly evolved in the few last decades. Humanity’s advancements in several realms of technology, innovation and engineering have propelled optimization techniques in logistics support greatly. Integration and immergence of tools like IoT and wireless data servers have also paved the way for remote operation in logistics
(Krishnan et al., 2024)
. Remote operations do not require any humans to be present on the spot of the operation. This opens up a lot of possibilities which can allow remote mining operations in areas where humans normally cannot access. With the growing prospects of artificial intelligence and machine learning algorithms, it is now possible to both optimize and automate many of the complex processes of logistics and mining, especially in remote operations. There are specific algorithms which are designed to find the best and most efficient route for saving time and resources like Dijkstra’s algorithm, Bellman-Ford algorithm, Floyd-Warshall algorithm etc. The algorithms which improve route efficiency is called Route Optimization Algorithms (ROA). ML algorithms are now considered as a very potent tool in the realm of logistics support and ML-based route optimization. This research paper will focus on an ML-based route optimization algorithm called A* Search algorithm. This research paper aims to explore the effectiveness of a machine learning algorithm to optimize route for better logistics support in a remote operation scenario. Mining has always been a risky endeavor for the people involved
(Fraser, 2023)
. Such solutions can help to explore some efficient ways for improved remote mining that does not require humans to be present. ML algorithms can greatly improve the route and effective storing processes in mining operations for maximizing storage and transportation.
Keywords:
Optimization Algorithms (ROA); Logistical Support; Inventory Management; Information Technology (Iot); Remote Mining Operation; Autonomous Machinery; ML Infused Robotics
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