Introducing to Industrial Engineering Students to Linear Programming with Pulp from Python: A Case Study in Peru
Carlos Sotomayor-Beltran1, Alexi Delgado2
1Carlos Sotomayor-Beltran, Imaging Processing Research Laboratory (INTI-Lab), Universidad de Ciencias y Humanidades, Lima, Peru.
2Alexi Delgado, Department of Engineering, Mining Engineering Section, Pontificia Universidad Católica del Perú – PUCP, Lima, Peru.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 749-752 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7226068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One core course in the curriculum of engineering industrial programs in Peruvian universities is operations research, which is regarding finding the optimal solution to real-life problems in a diverse range of industries (business, transport, agriculture, healthcare, etc.). In this paper the package PuLP from the programing language Python is presented as a computational tool that can help Peruvian industrial engineering students to solve optimization problems involving linear programming. An example about a fabric that produces beds and their accessories is presented. On the other hand, a function that needs to be maximized to obtain the best profit is defined along with its decision variables and some constraints. As a consequence of writing a program in Python using PuLP, the best values for the decision variables could be obtained. Furthermore, by algebraically solving the inequalities corresponding to the constraints and looking into the feasible region, the result obtained by this method was the same as using PuLP. Then, it could be suggested that the operation research courses can benefit the students by relying also in the use of this type of computational tools in view of the complexity that real-life linear optimization problems can have. In addition, the results of this work could contribute to improve the teaching-learning process in operations research courses within of engineering industrial programs in Peruvian universities
Keywords: linear Programming, Operations Research, Pulp, Python
Scope of the Article: Industrial Engineering