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

Document Type : SI: DBBD-2023

Authors

1 Associate Professor of Industrial Engineering Department of Industrial Engineering School of Engineering University of Kurdistan Pasdaran Bolvar Sanandaj, Iran

2 Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj,

3 Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

10.22059/ijms.2024.366021.676227

Abstract

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.

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Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)

Articles in Press, Accepted Manuscript
Available Online from 03 February 2024

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