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Ghaderi, Abdolsalaam, Ghahremani Nahr, Javid, Safari, Saba. (1402). Providing a Robust Heterogeneous Vehicle Fleet Routing Model Based on Artificial Intelligence of Things(AIoT). , (), -. doi: 10.22059/ijms.2024.366021.676227
Abdolsalaam Ghaderi; Javid Ghahremani Nahr; Saba Safari. "Providing a Robust Heterogeneous Vehicle Fleet Routing Model Based on Artificial Intelligence of Things(AIoT)". , , , 1402, -. doi: 10.22059/ijms.2024.366021.676227
Ghaderi, Abdolsalaam, Ghahremani Nahr, Javid, Safari, Saba. (1402). 'Providing a Robust Heterogeneous Vehicle Fleet Routing Model Based on Artificial Intelligence of Things(AIoT)', , (), pp. -. doi: 10.22059/ijms.2024.366021.676227
Ghaderi, Abdolsalaam, Ghahremani Nahr, Javid, Safari, Saba. Providing a Robust Heterogeneous Vehicle Fleet Routing Model Based on Artificial Intelligence of Things(AIoT). , 1402; (): -. doi: 10.22059/ijms.2024.366021.676227

Providing a Robust Heterogeneous Vehicle Fleet Routing Model Based on Artificial Intelligence of Things(AIoT)

Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 14 بهمن 1402
نوع مقاله: SI: DBBD-2023
شناسه دیجیتال (DOI): 10.22059/ijms.2024.366021.676227
نویسندگان
Abdolsalaam Ghaderi* 1؛ Javid Ghahremani Nahr2؛ Saba Safari3
1Associate Professor of Industrial Engineering Department of Industrial Engineering School of Engineering University of Kurdistan Pasdaran Bolvar Sanandaj, Iran
2Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj,
3Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
چکیده
This paper introduces a novel bi-objective routing model founded on AIoT principles. Our model not only aims to minimize vehicle transportation costs and prevent time window violations but also endeavors to mitigate environmental pollutants. Our study addresses the complex challenge of optimizing routes for heterogeneous vehicle fleets with Artificial Intelligence of Things (AIoT) 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
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تعداد مشاهده مقاله: 30


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