| Issue |
BIO Web Conf.
Volume 113, 2024
XVII International Scientific and Practical Conference “State and Development Prospects of Agribusiness” (INTERAGROMASH 2024)
|
|
|---|---|---|
| Article Number | 05020 | |
| Number of page(s) | 12 | |
| Section | Agricultural Production Management | |
| DOI | https://doi.org/10.1051/bioconf/202411305020 | |
| Published online | 18 June 2024 | |
Stochastic model for product supplier selection
1 Institute for Civil Defence and Emergencies, Davydkovskaya str., 7, Moscow, 121352, Russia
2 Moscow State University of Geodesy and Cartography, Gorokhovsky lane, 4, Moscow, 105064, Russia
3 JSC NPO Technomash named after S.A. Afanasyev, 3rd proezd of Maryina Roshcha, 40, Moscow, 127018, Russia
4 Belarusian National Technical University (BNTU), Minsk, Belarus
* Corresponding author: aldokukin@yandex.ru
mikhail.lomakingo@list.ru
y_niyazova@edu.miigaik.ru
syr@mail.ru
The aim of the article is to develop a model of rational choice of technology and equipment supplier under conditions of external environment uncertainty. The technical and economic requirements of the customer are considered as operational, i.e. each requirement can be evaluated by expert judgement or measured using one or another means of measurement. This problem belongs to the multi-criteria ones with diverse indicators, for which traditional methods of linear convolution of criteria do not function correctly. We present a stochastic model of product supplier selection based on the concept of suitability and stochastic dominance, as well as the principle of guaranteed result in the conditions of incomplete data represented by finite limited samples of supplier characteristics. Both univariate and multivariate cases of supplier selection are addressed within the model. Ultimately, the formulated models are converted into linear Boolean programming models, which can be effectively solved using conventional linear optimization software packages.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.