Providing a Robust Heterogeneous Vehicle Fleet Routing Model Based on Artificial Intelligence of Things (AIoT) | ||
| Interdisciplinary Journal of Management Studies | ||
| دوره 17، شماره 4، زمستان 2024، صفحه 1173-1188 اصل مقاله (1.06 M) | ||
| نوع مقاله: SI: DBBD-2023 | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2024.366021.676227 | ||
| نویسندگان | ||
| Abdolsalam Ghaderi* ؛ Javid Ghahremani Nahr؛ Saba Safari | ||
| Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran | ||
| چکیده | ||
| This paper introduces a novel bi-objective routing model based on Artificial Intelligence of Things (AIoT) principles. Our model not only aims to minimize vehicle transportation costs and prevent time window violations but also endeavors to mitigate environmental pollutants. This study addresses the complex challenge of optimizing routes for heterogeneous vehicle fleets usingAIoT technology. Analyzing the bi-objective model using AI tools (MOSCA and NSGA II), we unveil a fascinating trade-off: as energy consumption decreases, system costs increase. Employing robust optimization techniques, we validate the model's performance under pessimistic conditions characterized by rising uncertainty rates. Notably, heightened uncertainty correlates with increased objective function values. Through a series of diverse test cases, we observe that MOSCA demonstrates superior efficiency, notably outperforming in NP, MD, and T indices. Our findings offer valuable insights for practitioners, policymakers, and researchers in the domains of transportation optimization, AIoT, and environmental sustainability. | ||
| کلیدواژهها | ||
| Vehicle Routing؛ Artificial Intelligence of Things؛ Soft Time Window؛ Green Logistics؛ Robust Optimization Method | ||
| مراجع | ||
|
| ||
|
آمار تعداد مشاهده مقاله: 650 تعداد دریافت فایل اصل مقاله: 638 |
||