DYNAMIC FREIGHT PRICING BY ARTIFICIAL INTELLIGENCE AND MATHEMATICAL HYBRID SYSTEM

1 ELÇİ Tuğçe
Co-authors:
1 TÜRKER Ahmet Yesevi 1 GÜNEY Hasan 1 USTAOĞLU Ahmet 1 KANTAR Deniz
Conference:
CLC 2023 - Carpathian Logistics Congress, Wellness Hotel Step, Prague, Czech Republic, EU, November 8 - 10, 2023
Proceedings:
Proceedings CLC 2023 - Carpathian Logistics Congress
Pages:
136-141
ISBN:
978-80-88365-17-4
ISSN:
2694-9318
Published:
10th April 2024
Metrics:
16 views / 18 downloads
Abstract

In domestic road operations, the demand for increased efficiency and performance is on the rise. To enhance operational efficiency and minimize costs, it becomes crucial to generate dynamic pricing for routes through digital media. The primary objective, in line with our developed model, is to boost the profit margin by estimating freight prices on our digital platform based on factual data. These estimates are then transmitted to the operational units to reduce current freight costs. Our digital platform serves as a nexus for suppliers and customers in land transportation. In freight transportation, it's vital to provide cost-effective and dependable methods for locating cargo, aligning cargo owners with the right carriers, and quickly determining precise route pricing. We have established a digital learning system for freight pricing estimation using machine learning techniques and a mathematical model. We evaluated 41 regression models to select the artificial intelligence models for our prediction algorithm. Four regression models that demonstrated the best performance were chosen to build our artificial intelligence system. Acknowledging the combination algorithm developed by our company, the system operates within a continuous learning cycle and selects the most effective artificial intelligence models at specific intervals. Additionally, the mathematical model's purpose is to complement the results of artificial intelligence by using actual prices from similar routes to prevent potential errors. The degree to which the mathematical model refines the artificial intelligence's results is determined by our developed decision-making intelligent algorithm. The hybrid model's results were tested on real expeditions, confirming its success.

Keywords: Artificial intelligence, logistics, dynamic pricing, mathematical model, machine learning

© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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