Simulation-optimization of automated material handling systems in a healthcare facility

Abstract

Automated material handling systems are used in healthcare facilities to optimize material flow, minimize workforce requirements, reduce risk of contamination, and reduce work injuries. This paper develops a case study using data from Greenville Memorial Hospital (GMH) in South Carolina, USA. The case study is focused on the delivery of surgical case carts to operating rooms at GMH via Automated Guided Vehicles (AGVs). This study proposes a framework that integrates data analysis with system simulation and optimization. The study addresses the following research questions: (1) Redesign of the pathways: Do performance measures, such as travel time and task completion time, improve after a redesign of AGV pathways? (2) Operational fleet sizing: Do performance measures, such as travel time and task completion time, improve when the number of AGVs used daily is controlled by the volume of surgical cases? If this is true, then how many AGVs should be used daily? To address research question (1), we compare two AGV pathway designs via an extensive sensitivity analysis. To address research question (2), we use a simulation-optimization model to evaluate the performance of the system for different fleet sizes. Finally, we conduct a pilot study at GMH to validate the results of our analysis. This study indicates that the proposed solution, which uses a smaller fleet of AGVs than currently used at GMH, leads to significant reductions in congestion and travel times, and increased utilization of AGVs.