  Automated material handling systems are used in healthcare facilities to
optimize material flow, minimize workforce requirements, reduce the risk of
contamination, and reduce injuries. This study proposes a framework that
integrates data analysis with system simulation and optimization to address the
following research questions: (i) What are the implications of redesigning a
hospital's material handling system? (ii) What are the implications of
improving a hospital's material handling process? This paper develops a case
study using data from the Greenville Memorial Hospital (GMH) in South Carolina,
USA. The case study is focused on the delivery of surgical cases to operating
rooms at GMH via Automated Guided Vehicles (AGVs). The data analysis provides
distributions of travel times, AGV utilization, and AGV movement patterns in
the current system. The results of data analysis are integrated in a
simulation-optimization model that incorporates the size of AGV fleet and the
corresponding routes to improve system efficiency, increase AGV utilization,
and reduce congestion. To address research question (i), a redesign of AGV
pathways is evaluated to determine whether congestion is reduced. For research
question (ii), the implementation of a Kanban system is proposed to improve AGV
utilization by controlling the number of AGVs used daily, based on the volume
of surgical cases. An extensive sensitivity analysis, simulation-optimization
experiments, and a pilot study are conducted and indicate that the proposed
Kanban system leads to significant reductions in congestion and travel times
and increased utilization of AGVs.
