A Machine Learning-Based Decision Analytic Model for Optimal Route Selection in Autonomous Urban Delivery: The ULTIMO Project
DOI:
https://doi.org/10.31181/dmame7220241114Keywords:
Autonomous vehicles, Decision Support System, Multi-Criteria Decision-Making, ARWEN, WLD, Random Forest classifierAbstract
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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