The Evaluation of Transport Vehicle Suppliers

DOI

10.2991/aebmr.k.200201.013How to use a DOI?

Keywords

artificial intelligence, Doctus knowledge-based system, decision-making

Abstract

If in a twenty-five million city somebody changes three undergrounds, it is transportation. If you go to the pub by bicycle in a village of one hundred and fifty people, it is also transportation, but it is different. In a city of hundreds of thousands of people, transportation is already a common concern, and so it is left to professionals to figure it out. This requires decisions. The purpose of this study is to illustrate the preparation of a decision. During the preparation of the decision, the experts of a Hungarian transport company of the city of one hundred thousand people had to consider that the application of the ROIs they considered important was not applicable due to the public service character and the long-life cycle. Expectations were very diverse, and most were non-quantifiable. The leader of the company commissioned a team of experts to prepare the decision, consisting of an economist, a railway mechanical engineer, a transport system organizer engineer, an electrical engineer and an electric plant manager, as well as a city politician and civil servant. As a consultant, I strived for a transparent decision-making process, using the algorithm of a knowledge-based system based on Artificial Intelligence. This knowledge-based system can overcome the limitations of a person’s working memory and allow the exploration of the relationships between dozens of experts’ expectations.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Jolan Velencei
PY  - 2020
DA  - 2020/02/10
TI  - The Evaluation of Transport Vehicle Suppliers
BT  - Proceedings of the 1st International Conference on Emerging Trends and Challenges in the Management Theory and Practice (ETCMTP 2019)
PB  - Atlantis Press
SP  - 61
EP  - 64
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200201.013
DO  - 10.2991/aebmr.k.200201.013
ID  - Velencei2020
ER  -