[ Programmation Linéaire - Programmation en Python de l’optimisation par la Méthode de Simplexe : Cas de la production à la Brasserie SIMBA de Lubumbashi ]
Volume 36, Issue 1, April 2022, Pages 78–94
Mbuyi Wa Mbuyi Stephane1
1 Département des Mathématiques Informatiques, ISP Lubumbashi, RD Congo
Original language: French
Copyright © 2022 ISSR Journals. 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.
The purpose of this article is part of the logic of establishing a production plan in order to maximize turnover. In general, the resolution of mathematical programming problems (the production problem case of the BRASIMBA of Lubumbashi), aims to determine the best possible combination of limited resources (storage capacity for example), to achieve a certain objective. These allocations must maximize a so-called objective function, which can be a cost or a profit. To achieve our goal, we used the Python object-oriented programming language to computerize the so-called Simplex method, which starts from a basic feasible solution or acceptable basic solution that is not improved step by step. This method stems from linear programming, which is nothing more than a particularity of mathematical programming.
Author Keywords: Algorithm, programming language, maximization, economic function, simplex array, pivot.
Volume 36, Issue 1, April 2022, Pages 78–94
Mbuyi Wa Mbuyi Stephane1
1 Département des Mathématiques Informatiques, ISP Lubumbashi, RD Congo
Original language: French
Copyright © 2022 ISSR Journals. 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.
Abstract
The purpose of this article is part of the logic of establishing a production plan in order to maximize turnover. In general, the resolution of mathematical programming problems (the production problem case of the BRASIMBA of Lubumbashi), aims to determine the best possible combination of limited resources (storage capacity for example), to achieve a certain objective. These allocations must maximize a so-called objective function, which can be a cost or a profit. To achieve our goal, we used the Python object-oriented programming language to computerize the so-called Simplex method, which starts from a basic feasible solution or acceptable basic solution that is not improved step by step. This method stems from linear programming, which is nothing more than a particularity of mathematical programming.
Author Keywords: Algorithm, programming language, maximization, economic function, simplex array, pivot.
Abstract: (french)
L’objet de ce présent article s’inscrit dans la logique d’établir un plan de production de façon à maximiser les chiffres d’affaires. De manière générale, la résolution des problèmes de programmation mathématique (le cas de problème de production de la BRASIMBA de Lubumbashi), vise à déterminer la meilleure combinaison possible des ressources limitées (capacité de stockage par exemple), pour atteindre un certain objectif. Ces allocations doivent maximiser une fonction dite objectif, qui peut être un coût ou un profit. Pour atteindre notre objectif, nous nous somme servit du langage de programmation orientée objet python pour informatiser la méthode dite de Simplexe, qui part d’une solution réalisable de base ou solution de base admissible que l’on n’améliore pas à pas. Cette méthode découle de la programmation linéaire qui n’est autre qu’une particularité de la programmation mathématique.
Author Keywords: Algorithme, langage de programmation, maximation, fonction économique, tableau simplexe, pivot.
How to Cite this Article
Mbuyi Wa Mbuyi Stephane, “Linear Programming - Optimization programming in Python by the Simplex Method : Case of production at the Simba Brewery in Lubumbashi,” International Journal of Innovation and Applied Studies, vol. 36, no. 1, pp. 78–94, April 2022.
