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

Document Type : SI: DBBD-2023

Authors

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 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.

Keywords

Main Subjects

References

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Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
Volume 17, Issue 4
October 2024
Pages 1173-1188

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