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Inventory management of surgical instruments and material handling decisions of perioperative services are critical to hospitals’ and operating rooms’ (ORs) service levels and costs. However, efficiently coordinating these decisions is challenging due to their interdependence and the uncertainties faced by hospitals. These challenges motivated the development of this study to answer the following research questions: (R1) How does the inventory level of surgical instruments, including owned, borrowed and consigned, impact the service level provided by ORs? (R2): How do material handling activities impact the service level provided by ORs? (R3): How do integrating decisions about inventory and material handling impact the service level provided by ORs? Three discrete event simulation models are developed here to address these questions. Model 1, Current, assumes no coordination of material handling and daily inventory management operations. Model 2, Two Batch, assumes partial coordination, and Model 3, Just-In-Time (JIT), assumes full coordination. These models are verified and validated using real life-data from a partnering hospital. A thorough numerical analysis indicates that, in general, coordination of inventory management of surgical instruments and material handling decisions has the potential to improve the service level provided by ORs. More specifically, a JIT delivery of instruments used in short-duration surgeries leads to lower inventory levels without jeopardizing the service level provided.

For operating rooms (ORs) and hospitals, inventory management of surgical instruments and material handling decisions of perioperative services are critical to hospitals' service levels and costs. However, efficiently integrating these decisions is challenging due to hospitals' interdependence and the uncertainties they face. These challenges motivated the development of this study to answer the following research questions: (R1) How does the inventory level of surgical instruments, including owned, borrowed and consigned, impact the efficiency of ORs? (R2) How do material handling activities impact the efficiency of ORs? (R3) How do integrating decisions about inventory and material handling impact the efficiency of ORs? Three discrete event simulation models are developed here to address these questions. Model 1, Current, assumes no coordination of material handling and inventory decisions. Model 2, Two Batch, assumes partial coordination, and Model 3, Just-In-Time (JIT), assumes full coordination. These models are verified and validated using real life-data from a partnering hospital. A thorough numerical analysis indicates that, in general, coordination of inventory management of surgical instruments and material handling decisions has the potential to improve the efficiency and reduce OR costs. More specifically, a JIT delivery of instruments used in short-duration surgeries leads to lower inventory levels without jeopardizing the service level provided.

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.

Automated material handling systems are used to optimize material flow within healthcare facilities, minimize workforce requirements, reduce the risk of contamination, and reduce injuries. In this research, we are collaborating with a hospital which uses Automated Guided Vehicles (AGV) to perform tasks, such as moving surgical instruments, drugs, linen, food, trash, etc. This paper presents a framework to optimize the performance of AGVs in this hospital provided the physical limitations and business rules. Performance measures which are used to evaluate the system are travel time and AGV utilization. This framework integrates data analysis with system simulation and optimization. The data analysis provides distributions of travel times, AGV utilization and AGV movement patterns in the current system. The simulation model is implemented in Arena. An extensive sensitivity analysis and optimization experiments indicate that using a Kanban system greatly impacts the performance measures identified. We expect that when implemented, the proposed solution will result in up to 50% reduction of the average travel time, and up to 50% increase of utilization of AGVs.