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Iranian Journal of Management Studies، جلد ۱۷، شماره ۴، صفحات ۱۱۷۳-۱۱۸۸
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عنوان انگلیسی Providing a Robust Heterogeneous Vehicle Fleet Routing Model Based on Artificial Intelligence of Things (AIoT)
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Vehicle routing,Artificial Intelligence of Things,Soft Time Window,Green Logistics,Robust Optimization Method
نویسندگان مقاله Abdolsalam Ghaderi |
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

Javid Ghahremani Nahr |
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

Saba Safari |
Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

نشانی اینترنتی https://ijms.ut.ac.ir/article_95814_5debcf83f0cbeeefc5d536ec247b13c3.pdf
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