Forecasting suitable supplier for construction project using machine learning techniques
DOI:
https://doi.org/10.5377/nexo.v35i04.15549Keywords:
Supplier, freezing rate, construction project, machine learning techniquesAbstract
The aim of the research is to forecast the suitable suppliers for construction project using machine learning techniques. Firstly the librarian studies were conducted and research gap is extracted. Then innovation was determined. Based on the innovation a model for suitable supplier forecasting for construction project using machine learning techniques were provided. The model includes 12 entry variables and 1 output variable that include supplier performance. The model using 2 algorithm of artificial neuron network and support vector machine were conducted and the most influencing factors were determined using decision tree algorithm. The general comparison between artificial neuron network and support vector machine indicate the better performance of artificial neuron network based on decision tree. Based on decision tree results we can say that the supplier company income is considered as most important variable. The order change cost variable play the separator role in lower level. The life variables of companied and guarantees after company income and change cost of order play the main role.
Downloads
333
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Universidad Nacional de Ingeniería
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors who publish in Nexo Scientific Journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of the first publication under the license Creative Commons Attribution License, which allows others to share the work with a recognition of the authorship of the work and the initial publication in Nexo Scientific Journal.
- Authors may separately establish additional agreements for the non-exclusive distribution of the version of the work published in the journal (for example, in an institutional repository or a book), with the recognition of the initial publication in Nexo Scientific Journal.
- Authors are allowed and encouraged to disseminate their works electronically (for example, in institutional repositories or in their own website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.