Discrete-Event Simulation and System Dynamics for Management Decision Making

2014 ◽  
Keyword(s):  
Decision Making ◽  
System Dynamics ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Management Decision ◽  
Event Simulation ◽  
Management Decision Making

scholarly journals Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic

2021 ◽  
Vol 28 (1) ◽  
pp. e100248
Author(s):  
Prem Rajendra Warde ◽  
Samira Patel ◽  
Tanira Ferreira ◽  
Hayley Gershengorn ◽  
Monisha Chakravarthy Bhatia ◽  
...  
Keyword(s):  
Decision Making ◽  
System Dynamics ◽  
Predictive Models ◽  
Model Comparison ◽  
Prediction Models ◽  
Discrete Event ◽  
Resource Utilisation ◽  
Management Decision ◽  
Cross Model ◽  
Management Decision Making

ObjectivesWe describe a hospital’s implementation of predictive models to optimise emergency response to the COVID-19 pandemic.MethodsWe were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison.ResultsWe outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system’s executive policy response to implement a ‘hospital within a hospital’—one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population.ConclusionPredictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

Multi-Methodology Approach

2021 ◽  
pp. 98-128
Keyword(s):  
System Dynamics ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Multiple Methods ◽  
Soft Systems ◽  
Agent Based ◽  
Event Simulation ◽  
Pluralistic Approach ◽  
The Difference

The pluralistic approach in today's world needs combining multiple methods, whether hard or soft, into a multi-methodology intervention. The methodologies can be combined, sometimes from several different paradigms, including hard and soft, in the form of a multi-methodology so that the hard paradigms are positivistic and see the organizational environment as objective, while the nature of soft paradigms is interpretive. In this chapter, the combination of methodologies has been examined using soft systems methodologies (SSM) and simulation methodologies including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM). Also, using the ontological, epistemological, and methodological assumptions underlying the respective paradigms, the difference between SD, ABM, SSM; a synthesis of SSM and SD generally known as soft system dynamics methodology (SSDM); and a promising integration of SSM and ABM referred to as soft systems agent-based methodology (SSABM) have been proven.

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