A study on the economic order quantity model in uncertainty

Many uncertainties encountered in production processes cause difficulties in decision making and process management. Solution approaches that take into account these uncertainties are widely used in academic studies. In this thesis, some prediction models developed for the uncertainty environment have been examined, and the EOQ model containing grey numbers and defective products has been developed. Determining the optimum order quantity is the most important step in the EOQ model. Ordering cost and holding cost that affect the optimum order quantity are included in the model as an interval grey numbers. Demand quantity, which is another parameter used in determining the optimum order quantity, is obtained as a result of estimation. For the forecast of the demand value, the Moving Averages method which is one of the classical methods, the Rolling GM (1,1) model developed within the scope of the grey system theory, and the Chen (1996) and Chen (2002) forecast methods developed within the scope of fuzzy time series were examined and compared. The amount of demand for the EOQ model was determined for a furniture company using the forecast method that has an average percentage of the error rate of the least. The parameters affecting the total cost function are considered as interval grey numbers, and the future cost is estimated. In this study, the applicability of the grey system theory is tried to be shown in the decisions to be taken for the future in case of uncertainty.