2A: Introduction to Discrete Event Simulation
In this session, we explore how we can model pathway and queuing problems using a modelling method known as Discrete Event Simulation.
We explore how Discrete Event Simulations work, covering the terminology and concepts you will need to understand before you start designing your own simulation models.
This content is also covered in part 1 of the HSMA little book of DES.
2B: SimPy for Discrete Event Simulation - Part 1
In this session, we look at how we can use the SimPy Python package (which is built around generator functions) to build Discrete Event Simulations in Python.
We start with a very simple model in which patients arrive, are seen by a nurse, and then leave, before building up to multi-step models and models with branching pathways. We then explore how to record results from our simulation and explore the impact of changing the level of resources available or the distribution of inter-arrival times and activity durations.
This content is also covered in part 2 of the HSMA little book of DES.
2C: SimPy for Discrete Event Simulation - Part 2
In this session, we look at some more advanced DES concepts and how we can incorporate them in our SimPy models.
This includes
• warm up periods
• priority-based queuing
• resource unavailability
• Lognormal distributions
• reneging
• balking
• jockeying
This content is also covered in part 3 of the HSMA little book of DES.