Факультет математики, фізики та інформаційних технологій

Постійне посилання на фонд

https://dspace.onu.edu.ua/handle/123456789/37442

Переглянути

Перегляд Факультет математики, фізики та інформаційних технологій за Автор "Pengyang Liu"

Зараз показуємо 1 - 1 з 1
  • Ескіз
    Документ
    Information technology of the Optimizing Internal Railway Transportation Paths in Metallurgical Enterprises Using Dijkstra's Algorithm
    (Одеський національний університет імені І. І. Мечникова, 2024) Pengyang Liu
    This paper explores the theme of "Information Technology Based on Dijkstra Algorithm in Metallurgical Railways." It discusses the application of the Dijkstra algorithm to optimize the selection of railway transportation paths in the metallurgical industry, aiming to enhance transportation efficiency, reduce costs, and alleviate the workload of internal railway employees in metallurgical enterprises. With the continuous advancement of the metallurgical industry, the growth in production volume has led to a significant increase in the frequency of molten iron transportation, making the role of railway transportation increasingly important. However, the complex layout of railway lines within metallurgical plants, numerous switches, coupled with the high speed of shunting and the diversity of transportation demands, pose considerable challenges for railway path planning. The Dijkstra algorithm, as an efficient tool for shortest path searches, has demonstrated significant effectiveness in addressing route planning issues. Its potency stems from its ability to manage single source shortest path problems, its path recording mechanism, and its graphical representation advantages. The essence of this algorithm lies in its gradual expansion process: starting from the origin, it visits nodes in the graph one by one and calculates the shortest path from the starting point to each node. The Dijkstra algorithm not only effectively identifies the shortest path but also records the specific details of the path, providing comprehensive reference information for subsequent path selection. This paper initially outlines the core concepts of the Dijkstra algorithm and its applicability in the field of route planning. Following that, a path optimization strategy based on the Dijkstra algorithm is designed, tailored to the specific characteristics of metallurgical railway transportation. This strategy takes into account not only the distance aspect of the path but also comprehensively considers multiple dimensions, including safety, economic cost, time efficiency, and expansion potential, all in an effort to find the optimal transportation solution. By constructing a mathematical model and combining it with specific practical cases, the algorithm's application value and practical effectiveness are verified. In practical applications, the Dijkstra algorithm can effectively tackle various issues encountered in metallurgical railway transportation. For instance, it can assist dispatchers in selecting the optimal path by accurately calculating the cost of each path, thereby reducing unnecessary travel distances and saving time and resources. Moreover, this method exhibits excellent flexibility and adaptability, capable of adjusting relevant parameters according to different transportation requirements, providing an efficient and stable solution for railway transportation in the metallurgical industry. For example, when rapid response to emergencies or adjustments to transportation plans are needed, the Dijkstra algorithm can quickly recalculate the path to ensure the smooth progress of transportation tasks. Furthermore, the application of the Dijkstra algorithm also lays the groundwork for the construction of subsequent intelligent dispatching systems. By integrating with big data analysis, Internet of Things (IoT) technology, and Artificial Intelligence (AI), the Dijkstra algorithm can achieve dynamic path optimization in more complex environments. For example, using real time data monitoring and predictive analysis, potential transportation bottlenecks can be identified in advance, allowing for preventive measures; through machine learning algorithms, path selection strategies can be automatically adjusted, further enhancing the intelligence and precision of path planning. In summary, path optimization using the Dijkstra algorithm not only enhances the precision and efficiency of path planning but also provides more flexible and reliable technical support for railway transportation in the metallurgical industry. This contributes to enhancing the competitiveness of enterprises and promoting the sustainable development of the entire industry. In the future, with the continuous advancement of information technology, the Dijkstra algorithm will play a greater role in metallurgical railway transportation, promoting the diversified development of shortest path algorithm selection.