Introduction to discrete-event simulations

Discrete Event Simulation in Python

Diogo Costa (PhD, MSc)

Adjunct Professor, University of Saskatchewan, Canada & CEO of ImpactBLUE-Scientific

Discrete-event models vs. continuous models

Continuous models

Run at fixed time steps
Time is a dummy variable
- Time not updated inside the loop
Use of time for-loops
```
for t range(time_range):
```

Discrete-event models

Run at variable time steps
- Run/Schedule discrete events with different durations over time
Time is a state-variable
- Time is updated inside the while-loop
- Time is not a dummy variable

Often uses while-loops

while (time < 365):
time += process_duration

Components of discrete-event models

State-variables
Clock
Event list (or Process list)
Ending condition(s)

Model components

Example: Taxi company model

while (time < 10):

    # Process 1
    time_duration_1 = manage_requests()
    time += time_duration_1

    # Process 1
    time_duration_2 = dispatch_taxi()
    time += time_duration_2

Ending condition

while (time < 10)

Condition(s) that terminates simulation

Model components

Example: taxi company model

while (time < 10):

    # Process 1
    time_duration_1 = manage_requests()
    time += time_duration_1

    # Process 1
    time_duration_2 = dispatch_taxi()
    time += time_duration_2

Clock

time += time_duration_1
time += time_duration_2

time tracks current simulation time
Clock skips to next event start time as simulation proceeds
Time hops because events are instantaneous

Model components

Example: taxi company model

while (time < 10):

    # Process 1
    time_duration_1 = manage_requests()
    time += time_duration_1

    # Process 1
    time_duration_2 = dispatch_taxi()
    time += time_duration_2

State-variable

time

Reduce time between customer calling taxi and being dropped off
Variables that characterize system outputs and efficiency

Let's practice!

Discrete Event Simulation in Python