THE ECONOMIC PRODUCTION QUANTITY (EPQ) MODEL CONSIDERING INFLATION, TIME VALUE OF MONEY AND INVESTMENT VARIABLE IN PRODUCTION CAPACITY

The classical inventory models do not consider the real-world competitive situations and management desire to enhance the system and economic parameters such as inflation and time value of money. Studies show that the management wishes an investment in system components in order to improve them and gain a competitive advantage over other businesses. Moreover, the value of system financial resources depends on the time that the use and cash flow of different time is not aggregate. Due to inflation, prices become volatile and change constantly. Therefore, disregard for inflation and time value of money parameters does not lead to optimal ordering policy for inventory systems in reality. For these reasons, it is necessary to consider these components for an inventory system.Previous researchers have conducted various research studies on the performance of inventory systems from various perspectives. Many of these papers did not consider economic parameters and also production environment. During our studies, this study found a gap in the literature that was related to the enhancement of the production capacity in inventory systems in an economic structure. The present paper presents a literature review of time value of money, inflation, and investment in inventory systems and, then, investigates economic production quantity (EPQ) model considering inflation and time value of money in situations, where there is a possibility of investment as a decision variable with the aim of increasing production capacity. The goal of this model is to find economic production quantity and optimal quantity of investment in production capacity so that the total profit function, consisting of system revenues and costs, is optimized. Due to complicated behavior of the proposed model, it is not possible to solve this model analytically. Therefore, a grid search algorithm is proposed for solving the model. The solving algorithm process is investigated by a numerical example. Sensitivity analysis is carried out by changing various parameters. Finally, it is concluded that there is an ascending relationship among the total profit function and the number of algorithm iterations, grid factor, demand rate, selling price, and inflation rate of price; there is also a descending relationship among the total profit function and ordering price, purchase price, rate of return, and inflation rate of sales. At the end, some possible research problems are proposed for the future.