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


Article Information

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

Authors: Sana Shabbir, Aamir Shahzad, Tasadduq Niaz, Sidra Ashraf, Namal, Faheem Khan

Journal: International Journal of Innovations in Science & Technology

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31
Y 2023-07-01 2024-09-30
Y 2021-07-01 2022-06-30

Publisher: 50SEA JOURNALS (SMC-PRIVATE) LIMITED

Country: Pakistan

Year: 2025

Volume: 7

Issue: 4

Language: en

Keywords: OptimizationPythonLinear Programming (LP)Production Planning ProblemSciPy

Categories

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.


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