A Machine Learning-Based Decision Analytic Model for Optimal Route Selection in Autonomous Urban Delivery: The ULTIMO Project

Shervin Zakeri; Dimitri Konstantas; Prasenjit Chatterjee; Ali Vaez Jalali

doi:10.31181/dmame7220241114

Authors

Shervin Zakeri Geneva School of Economics and Management, University of Geneva, 1211 Geneva, Switzerland https://orcid.org/0000-0002-3430-100X
Dimitri Konstantas Geneva School of Economics and Management, University of Geneva, 1211 Geneva, Switzerland https://orcid.org/0000-0002-2664-9348
Prasenjit Chatterjee Department of Mechanical Engineering, MCKV Institute of Engineering, West Bengal- 711204, India https://orcid.org/0000-0002-7994-4252
Ali Vaez Jalali Computer Engineering Department, Shomal University, Amol, Iran https://orcid.org/0009-0005-1490-2130

DOI:

https://doi.org/10.31181/dmame7220241114

Keywords:

Autonomous vehicles, Decision Support System, Multi-Criteria Decision-Making, ARWEN, WLD, Random Forest classifier

Abstract

In this research, we introduce a Decision Support System (DSS) that incorporates two multi-criteria decision-making (MCDM) techniques: the Alternatives Ranking with Elected Nominee (ARWEN) and the Win-Loss-Draw (WLD) methods. This system benefits from both methods’ advantages to address the challenge of selecting optimal routes for autonomous urban deliveries. The primary objective of this study is to establish a comprehensive framework to assist decision-makers in selection of the optimized strategy. This paper presents not only the implementation codes but also validates the results and conducts a sensitivity analysis. Furthermore, the DSS is applied to a numerical example to demonstrate its practical utility in real-world scenarios.

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