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A Distribution Network Design Model Using Data Classification and Fleet Optimization | ||
| Advances in Industrial Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 05 مهر 1404 | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2025.386390.1927 | ||
| نویسندگان | ||
| Mohammad Amirahmadi1؛ Hamid Esmaili* 2؛ kia parsa1؛ amin mostafaee3 | ||
| 1Department of Industrial Engineering, Faculty of Engineering, Islamic Azad University, North Tehran Branch, Tehran, Iran | ||
| 2Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| 3Department of Mathematics, Faculty of Sciences, Islamic Azad University, North Tehran Branch, Tehran, Iran | ||
| چکیده | ||
| This study seeks to bridge the existing gaps in previous researches by introducing a comprehensive data-driven network design model. The process begins with an in-depth analysis of customer demand, utilizing unsupervised learning algorithms to gain valuable insights into consumer behavior. This analysis will help identify demand levels across various geographical regions while uncovering patterns that fluctuate over time. These insights will serve as essential inputs for the network design model. To facilitate effective data classification and analysis, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm will be employed, enabling accurate estimation of customer demand based on innovative parameters. Building upon these findings, a new mathematical model will be created that incorporates fleet optimization constraints. Importantly, during this modeling process, emphasis will be placed not only on optimizing the number, location, and capacity of facilities but also on refining fleet types and their compositions to enhance overall efficiency. Due to the complexity of the model, it will be solved using various numerical case problems. Due to the complexity of the model, it will be solved using various numerical case problems. The results demonstrate that the proposed data- driven model achieves an average profit improvement of 10-15% compared to traditional non- clustered approaches. Furthermore, the model yields noticeable cost savings of approximately 8- 12% in transportation and fleet-related expenses. Furthermore, the integrated nature of model allows for an examination of key parameters to extract valuable managerial insights, demonstrating the synergy between data-driven clustering and mathematical optimization for distribution network design. | ||
| کلیدواژه‌ها | ||
| Machine-learning؛ Fleet optimization؛ DBSCAN algorithm؛ Demand pattern recognition | ||
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آمار تعداد مشاهده مقاله: 26 |
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