Optimization of Production Planning with Python’s SciPy: A Computational Study

Sana Shabbir, Aamir Shahzad, Tasadduq Niaz, Sidra Ashraf, Namal, Faheem Khan ()
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Sana Shabbir, Aamir Shahzad, Tasadduq Niaz, Sidra Ashraf, Namal, Faheem Khan: Faculty of Sciences,Superior University Lahore,Lahore54000,Pakistan. Department of Mathematics,Baba Guru Nanak University,Nankana Sahib39100,Pakistan. Department of Mathematics,University of Sargodha,Sargodha40100,Pakistan. Department of Mathematics,TheIslamia University of Bahawalpur,Bahawalpur63100,Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 4, 2288-2298

Abstract: Production planning optimization is the act of effectively distributing limited resources, including labor, materials, and equipment, to achieve production targets while optimizing profit and reducing waste. This study analyzes how optimization methods can be applied to production planning models in the cooking oil sector, with a particular emphasis on how linear programming (LP) can be used to handle usable quality limitations to maximize gross profit. The goal of this study is to find the best values for decision variables across a variety of inventory-based production frameworks. It is important in a manufacturing zone where input bound must be weighed against consumer needs, such as the industry of cooking oil. In order to provide a computational method for determining the perfect production levels, the study establishes a linear programming (LP) model and solves it using Python’s SciPy package. This optimization method uses objective functions involving dense matrices and numerical equations to solve the production planning problem. In calculating output levels and profit margins, the numerical results show a significant convergence, rating the effectiveness and credibility of the suggested approach in providing the optimal solution for practical industrial planning.

Keywords: Optimization; Linear Programming (LP); Python; Production Planning Problem; SciPy (search for similar items in EconPapers)
Date: 2025
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