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Abstract

Driven by the significant computational advancements of the last decade and their profound impact on specialized software, complex problems involving thousands of variables and constraints are now categorized as small-scale, with contemporary solutions effectively addressing models with tens or hundreds of thousands of continuous variables. Leveraging these technological strides and the high-speed data processing capabilities of modern hardware, ready-made software for operations research has been developed to tackle administrative challenges, facilitating rapid application and allowing researchers to focus on problem formulation, methodology, and sensitivity analysis to determine potential model impacts. To optimize time and effort while prioritizing critical stages of problem-solving, this study explores the use of specialized software to generate production plans. Upon examining the production workflow for ten products at Al-Salam Company, it was observed that planning is currently conducted manually using rudimentary mathematical methods reliant on personal intuition and experience. While such expertise is valuable, it lags behind global scientific advancements due to a prevailing misconception regarding the impracticality of applying scientific models and software in production planning, often attributed to sudden fluctuations in capacity, demand, or inventory. Consequently, this research demonstrates the utility of Win.Q.S.B software in issuing a production plan for Al-Salam Company, detailing the mechanism for formulating and inputting the problem, modifying the mathematical model, and interpreting solution results through two-dimensional and three-dimensional graphical representations. Operating within the Windows environment, this model proves its capacity to absorb and interact with real-time and future variables affecting industrial organizations with high speed and flexibility, offering a scalable framework that can be adapted to other manufacturing facilities by adjusting specific parameters and coefficients.

DOI

10.33095/jeas.v13i48.1250

Subject Area

Statistical

First Page

314

Last Page

237

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