Stock optimization based on simulation meta-modeling for multiple item case in hospital systems

In first stage of this study, a single-item inventory system for hospital systems was studied using a continuous review (r, Q) policy. Demand for any material of patients in a hospital depends on random arrival rate and random length of stay in units. Therefore, the demand for any material is a stochastic process. This stochastic process makes determining optimal levels for r and Q more difficult. In this study, a simulation meta-model is constructed to obtain equations for the average on hand inventory and average number of orders per year. Then, the optimal levels of r and Q, which minimize the total cost, are obtained using an integer non-linear model and a case study is presented.In second stage, inventory problem for multiple-item case in hospital systems was investigated by combining the continuous and periodic control policies under the stochastic demand and instantaneous lead time.Finally, distribution of demand during lead time was obtained both using analytical approach and simulation model under stochastic demand and lead time. The model was enhanced to determine the reorder quantities of different service levels.