Simulations in Python: Discrete Event Simulation with SimPy
summary
Builds an M/M/1 queue in SimPy + simpy_helpers inside a Jupyter notebook, using numpy draws for exponential interarrival and service times (mean service 20) and running about 200 entities through a single-capacity server. Measures total, waiting and service times, compares random seeds 34 and 35, and plots histograms and queue size over time. Implements a priority flag so priority customers wait less, and the code runs and produces the reported metrics.
AI-written summary · about
chapters (16)
- 0:00Python simulations with SimPy
- 1:06What is simulation?
- 2:53Types of simulations
- 5:00Discrete event simulation
- 7:06Queuing theory basics
- 11:19M/M/1 queuing model
- 16:37Simulation model components
- 20:53Theory questions
- 23:02Introducing SimPy
- 26:20Setting up environment
- 28:09Defining entities
- 33:03Resources and service times
- 34:30Source and generation
- 36:38Running simulation
- 39:08Analyzing outputs
- 42:46Summary & Q&A
related talks
31mUnderstanding exchange network dynamics with Python
01h 18msupplyseer: Computational Supply Chain with Python
33mUntangling the complexity of demand forecasting models: building a Market Simulator
19mAn Experimentation and Analytics Framework for Large-Scale AI Operations Platforms
30mMultibody Simulation Using sympy, scipy and vpython
30mDisease Modeling with Scipy and PyMC
19mA careful walk through probability distributions, using Python
29mLearning Outcomes in Queueing Systems