Operational Research in the Stochastic Programming and Simulation
Lead Research Organisation:
University of Southampton
Department Name: Sch of Mathematical Sciences
Abstract
Hospital accident and emergency department (ED) operations must respond to the changing nature of their external environment. The demand on ED resources, the supply of in-patient beds and the ED process all dramatically affect quality and safety within a department. The proposed doctorate will investigate real time simulation of ED operations and use these to optimise the management of the department. We will be working in collaboration with Basingstoke Hospital ED.
Real time simulation is a novel area, with scope for exciting, high-impact research. In real time simulation, the simulation model automatically draws data from the real system, enabling accurate forecasting of future behaviour. This was first used in manufacturing (e.g. Xu et al. 2016) but has only started to be implemented in health systems to forecast future behaviour (e.g. Hoot et al. 2009). Because it is able to combine the current state of the ED with detailed modelling of the department procedures and the current state of the hospital, it enables different management processes to be compared in real time.
The detailed process modelling necessary for the simulation model of the ED department will be achieved using discrete event simulation (DES. This will allow different approaches for managing the ED over subsequent weeks (e.g. staff rosters, routing, implementation of emergency measures) to be compared quickly. We anticipate the student developing novel methodology for finding the optimal set up for the ED, taking into account multiple performance indicators. The novel contribution is likely to be automation of the optimisation and the presentation of results.
Real time simulation is a novel area, with scope for exciting, high-impact research. In real time simulation, the simulation model automatically draws data from the real system, enabling accurate forecasting of future behaviour. This was first used in manufacturing (e.g. Xu et al. 2016) but has only started to be implemented in health systems to forecast future behaviour (e.g. Hoot et al. 2009). Because it is able to combine the current state of the ED with detailed modelling of the department procedures and the current state of the hospital, it enables different management processes to be compared in real time.
The detailed process modelling necessary for the simulation model of the ED department will be achieved using discrete event simulation (DES. This will allow different approaches for managing the ED over subsequent weeks (e.g. staff rosters, routing, implementation of emergency measures) to be compared quickly. We anticipate the student developing novel methodology for finding the optimal set up for the ED, taking into account multiple performance indicators. The novel contribution is likely to be automation of the optimisation and the presentation of results.
Organisations
People |
ORCID iD |
Christine Currie (Primary Supervisor) | |
Alexander Heib (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/V520056/1 | 17/09/2020 | 16/10/2025 | |||
2459475 | Studentship | EP/V520056/1 | 19/10/2020 | 01/07/2025 | Alexander Heib |