الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
This work introduces a new application of aggregate production planning (APP) in the manufacturing of carbon steel pipes and hot induction bends. The strategic significance of this industry necessitates a focus on productivity enhancement and economic performance optimization. To achieve this, the study proposes an APP optimization model, with the objective of increasing profitability by minimizing both production and inventory costs. The model is formulated as a deterministic, multi-product, multi-period model. Three alternative optimization techniques were applied: linear programming, genetic algorithms, and hybrid genetic algorithms, as a case study in a steel pipes manufacturing company. The results indicate that linear programming yields the same results as hybrid genetic algorithms but in less time. Moreover, a feasibility study was conducted to evaluate the effectiveness of the proposed model against the original planning system in the company, revealing a 12% decrease in overtime wages and a 9% increase in profit.
Furthermore, a real-life case study was conducted to demonstrate the model’s capabilities in real-world scenarios. After the completion of the projects, the actual production cost, total wages cost increased by 1%, 4% respectively, and profit decreased by 6%, compared to the planned. Additionally, the actual inventory cost was found to be 6% less than the planned cost. These results further demonstrate the feasibility and effectiveness of the proposed APP optimization model in a real-world setting.
Although the proposed APP optimization model successfully achieved results in accordance with the company’s strategic goals, there are certain limitations that need to be addressed for further enhancements in its agility and dynamism. Specifically, the model should be made more dynamic and vital to better handle unexpected breakdowns, unplanned maintenance work, possible delays in raw material delivery, and fluctuations in raw material and consumables costs during the planning horizon. By addressing these limitations, the model can further improve its effectiveness in addressing aggregate production planning problems and provide more accurate results.