Replenishment Process Simulation and Analysis for Pharmacy Robotic Dispensing Systems

The objective of this paper is to exploit a discrete-event simulation model as a tool for analyzing the replenishment process for a large-scale Robotic Dispensing System (RDS) with an actual pharmacy-based dataset. The RDS is a critical automated system to realize pharmacy automation in the Central Fill Pharmacy System (CFPS), which is a prescription filling system to process large volumes of prescription demand. To guarantee the high productivity of the RDS, the replenishment process should be optimized under limited resources to ameliorate the detrimental impact of errors, which are caused by the shortage of pills in the dispenser by operational replenishment delays. Although the significance of replenishment process optimization has been recognized, there is still little research on it due to complex interactions between automated systems and operators in analyzing the replenishment process. To overcome these challenges and deal with the urgent need for modeling the replenishment process, a simulation-based approach is used to uniquely design the replenishment process with manual operations, including machine-to-machine, human-to-machine, and human-to-human interactions by reflecting real-world practice. This paper aims to develop simulation models for accurately capturing the replenishment process integrated with the RDS operations. Multiple performance metrics are used to analyze system performance, and proper priority and staff working strategies are discussed to improve the system performance. Insights for practitioners are provided to design and manage efficient replenishment operations under limited resources.