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The University of Southampton
CORMSIS Centre for Operational Research, Management Sciences and Information Systems

Symbiotic Simulation for the Operational Management of Inpatient Beds Event

13:00 - 14:45
19 April 2018
Building 2, Room 3043

For more information regarding this event, please email Christine Currie at .

Event details

In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This talk shows how symbiotic simulation can help hospitals with important short-term operational decision making. In symbiotic simulation, the simulation reads data from the physical system regularly (i.e. to re-initialise the system state and if necessary, update the decision variables and/or simulation parameters). Then the simulation outputs are used for what-if analysis, and an external decision maker can choose to change the behaviour of the physical system. In other words, the simulation system indirectly controls the physical system via the external decision maker. This talk demonstrates how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. The benefit of our symbiotic simulation is demonstrated by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census.

Speaker information

Stephan Onggo,Trinity Business School, University of Dublin,is an Associate Professor of Data Analytics. His research interests lie in the areas of predictive analytics using simulation (symbiotic simulation, hybrid modelling, agent-based simulation, discrete-event simulation) with applications in operations and supply chain management (e.g. hospital, manufacturing, transportation, agri-food). He is the associate editor for the Journal of Simulation and the secretary of The OR Society’s Simulation Special Interest Group. He is the coordinator for agent-based simulation track at the Winter Simulation Conference 2018, stochastic modelling and simulation stream at EURO18 conference and simulation stream at OR60 Conference.

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