دانلود رایگان مقاله تولید و کنترل موجودی قطعات خودرو بر اساس توزیع احتمالاتی پیش بینی موجودی

Abstract

Bayesian networks are probabilistic models used for prediction and decision making under uncertainty. The delivery quantity, the production quantity, and the inventory are changing according to various unexpected events. Then the prediction of a production inventory is required to cope with such irregular fluctuations. This paper considers a production adjustment method for an automobile parts production process by using a dynamic Bayesian network. All factors that may influence the production quantity, the delivery quantity, and the inventory quantity will be handled. This study also provides a production schedule algorithm that sequentially adjusts the production schedule in order to guarantee that all deadlines are met. Furthermore, an adjusting rule for the production quantities is provided in order to maintain guaranteed delivery.

4. Conclusion

The DBN model was constructed for a production inventory management of an auto parts assembly line to handle the irregularly changing delivered product volume, production volume, and inventory volume. We determined the causes of change for probabilistically changing delivered product volume and production volume through factor analysis, and converted these causes into nodes to a probabilistic dependent relation that was presented through a graph. Also, in order to develop a production plan reflecting such efforts, we suggested a production inventory management method that would accordingly adjust production plans to each coming period and optimally guarantee delivery deadlines. Production plans themselves would maintain optimal inventory volume by calculating the predicted probability distribution based upon accumulative data. Finally, we presented a reduced cost of inventory management by comparing them prior and after adjusted production plans, and comparing the real cost of that year.