A Pharmaceutical Inventory Model for Varying Deteriorating Items with Price Sensitive Demand and Partial Backlogging Under the Effect of Learning

Abstract

Most existing studies of the pharmaceutical inventory models considered constant demand and unvarying rate of deterioration which is not logical in the context of healthcare industries. So, we proposed a pharmaceutical inventory model with price dependent demand and varying rate of deterioration that follows two parameters Weibull distribution. The shortages during stock out assumed to be partially backlogged and the backlogging rate is assumed to be time dependent. The learning effect on different associated cost (holding and ordering) has been applied which makes the study realistic. The purpose of the development of this model is to optimize the total average cost of the system by computing the optimal time interval and optimal ordering quantity. Finally, a numerical example is taken to support the model and comprehensive sensitivity analysis has also been conducted to analyze the effect of changes in the optimal solution.