We have developed a Python package to support discrete-event simulation (DES) and monte-carlo simulation education and applied simulation research. Features:
Implementation of classic Optimisation via Simulation procedures such as KN, KN++, OBCA and OBCA-m
Theoretical and empirical distributions module that includes classes that encapsulate a random number stream, seed, and distribution parameters.
An extendable Distribution registry that provides a quick reproduible way to parameterise simulation models.
Implementation of Thinning to sample from Non-stationary Poisson Processes (time-dependent) in a DES.
Automatic selection of the number of replications to run via the Replications Algorithm.
EXPERIMENTAL: model trace functionality to support debugging of simulation models.
You can find the package on GitHub, PyPI, and Conda
Package documentation
The sim-tools documentation is available at https://tommonks.github.io/sim-tools/.
This page was written by Amy Heather and reflects her interpretation of this work, which may not fully represent the views of all project authors or affiliated institutions.