| Autor: |
Khasanah, Mufida Nur, Asih, Hayati Mukti |
| Předmět: |
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| Zdroj: |
Proceedings of the International Conference on Industrial Engineering & Operations Management; 9/14/2021, p1998-2007, 10p |
| Abstrakt: |
Inventory is one of the essential parts in a shop floor, especially in the chicken slaughter houses industry. The uncertain customer demand affects the uncertain raw materials (live chicken). So, to prevent the opportunity loss in business, the availability of live chicken is unavoidable. It affects the high inventory cost. In addition, the high risk of chicken death makes the problem more complicated. Therefore, this research is proposed to minimize the total inventory cost under demand uncertainty by optimizing the economic order quantity (EOQ). This study develops simulation optimization by integrating the Monte Carlo simulation and the Genetic Algorithm. This model optimizes the value of reorder point and reorder quantity in order to minimize the total inventory cost. Some experiments consider the analytical solutions and heuristic by varying crossover, mutation, and population values to provide a global optimum. The result shows the proposed solution reduces 38.95% from the existing total inventory cost. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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