A Comparative Study of Optimization Techniques for Aggregate Production Planning Applied in the Steel Pipes Industry | ||
| Port-Said Engineering Research Journal | ||
| Article 8, Volume 27, Issue 3, September 2023, Pages 97-111 PDF (810.34 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/pserj.2023.212371.1243 | ||
| Authors | ||
| Mostafa Ali Aboelseod* 1; Shaban Abdou2; Shady Aly3; Hanan Kamel Kouta4 | ||
| 1Department of Production Engineering and Mechanical Design, Faculty of Engineering, Port Said University, Port Said, Egypt | ||
| 2Prof. of Production Technology, Faculty of Eng., Port Said University. | ||
| 3Department of Mechanical Engineering, Faculty of Engineering, Helwan University ,Cairo, Egypt | ||
| 4Department of production engineering and mechanical design, Faculty of Engineering, Port Said University, Port Said, Egypt | ||
| Abstract | ||
| This study introduces a new application of aggregate production planning (APP) in the manufacturing of carbon steel pipes and hot induction bends. Given the strategic importance of this type of industry, enhancing productivity through cost-effectiveness, and economic performance optimization has become crucial in such industry. The study proposes an APP optimization model that is both inspiring and realistic, aimed at increasing profitability by minimizing both production and inventory costs. The model is formulated as a deterministic, multi-product, multi-period model, and 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. Additionally, a feasibility study evaluated 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. | ||
| Keywords | ||
| Aggregate Production Planning (APP); Deterministic Demand; Steel Pipes Industry; Mathematical Programming; Genetic Algorithms | ||
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