Improving Simulation Optimization Run Time When Solving for Periodic Review Inventory Policies in a Pharmacy

Abstract

Pharmaceutical drugs are critical to patient care, but demand and supply uncertainties in this inventory system make decision-making a challenging task. In this paper, we present a simulation-optimization model that determines near-optimal $(s, S)$ periodic review inventory policies that minimize the expected cost per day. The model accounts for perishability, positive lead time, stochastic demand, and supply disruptions. We implement a Binary Grid-Search algorithm which uses the structure of the objective function to quickly solve the simulation-optimization model. The numerical results illustrate how the Binary Grid-Search algorithm performs 21 times faster (when performing 10,000 replications) in terms of run time when compared to an Exhaustive Grid-Search, without sacrificing solution accuracy. This paper provides an efficient method to solve for the near-optimal $(s, S)$ periodic review inventory policies which is essential in the pharmacy inventory system that handles thousands of different drugs.