Development of a Dynamically Adaptable Routing System for Data Analytics Insights in Logistic Services

Author:

Tsoukas Vasileios1ORCID,Boumpa Eleni1ORCID,Chioktour Vasileios1ORCID,Kalafati Maria1ORCID,Spathoulas Georgios1ORCID,Kakarountas Athanasios1ORCID

Affiliation:

1. Department of Computer Science and Biomedical Informatics, University of Thessaly, 35 131 Lamia, Greece

Abstract

This work proposes an effective solution to the Vehicle Routing Problem, taking into account all phases of the delivery process. When compared to real-world data, the findings are encouraging and demonstrate the value of Machine Learning algorithms incorporated into the process. Several algorithms were combined along with a modified Hopfield network to deliver the optimal solution to a multiobjective issue on a platform capable of monitoring the various phases of the process. Additionally, a system providing viable insights and analytics in regard to the orders was developed. The results reveal a maximum distance saving of 25% and a maximum overall delivery time saving of 14%.

Funder

European Regional Development Fund of the European Union

Publisher

MDPI AG

Reference33 articles.

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