Please use this identifier to cite or link to this item:
https://hdl.handle.net/2445/195587
Title: | Enhancing production and sale based on mathematical statistics and the genetic algorithm |
Author: | Nestic, Snezana Aleksic, Aleksandar Gil Lafuente, Jaime Ljepava, Nikolina |
Keywords: | Gestió de la producció Gestió de vendes Estadística matemàtica Algorismes genètics Production management Sales management Mathematical statistics Genetic algorithms |
Issue Date: | Apr-2022 |
Publisher: | Univerzitet u Kragujevcu |
Abstract: | Enhancing production and sale has a very significant effect on the competitive advantage of any production enterprise. In practice, especially in companies with highly diversified production, products have a different impact on generating revenue. Therefore, operational management pay attention to the products of the utmost importance. The Pareto analysis is the most broadly used product classification method. It can be said that the results obtained by this analysis are still very burdened by decision-makers' subjective attitudes. This paper proposes a model for selecting products with the biggest impact on generating revenue in an exact way. In the model's first stage, whether there is a linear relationship between volume demand and a discounted amount is analyzed applying mathematical statistics methods. In the second stage, the Genetic Algorithm (GA) method is proposed so as to obtain a near-optimal set of the most important products. The proposed model is shown to be a useful and effective assessment tool for sales and operational management in a production enterprise. |
Note: | Reproducció del document publicat a: https://doi.org/10.5937/ekonhor2201057N |
It is part of: | Ekonomski Horizonti, 2022, vol. 24, num. 1, p. 53-68 |
URI: | https://hdl.handle.net/2445/195587 |
Related resource: | https://doi.org/10.5937/ekonhor2201057N |
ISSN: | 2217-9232 |
Appears in Collections: | Articles publicats en revistes (Empresa) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
731270.pdf | 1.04 MB | Adobe PDF | View/Open |
This item is licensed under a
Creative Commons License