Shelf-space optimization models in decentralized automated dispensing cabinets

Abstract

We propose a mixed integer programming (MIP) model to help clinicians store medications and medical supplies optimally in space-constrained, decentralized Automated Dispensing Cabinets (ADCs) located on hospital patient floors. We also propose a second MIP model that addresses human errors associated with the selection of pharmaceuticals from floor storage, and not only selects the best set of medications for storage but also determines their optimal layout within the cabinet. To improve the computational performance of these MIP models, we investigate several valid inequalities and relaxations that allow us to solve large, real-world instances in reasonable times. These models are applicable to very general ADCs and are illustrated using real-world data from ADCs at hospitals. Our results indicate that using these models can significantly reduce the time spent by clinical staff on routine logistical functions, while making efficient use of limited space and decreasing risks associated with errors in the selection of medication.