simpy
Category: data-science-and-ml · License: MIT · Version: 1.4 · Includes scripts
Tags: discrete-event simulation, simulation, queueing, operations research
When to use it
Builds, tests, and analyzes bounded process-based discrete-event simulations in Python with SimPy 4.1.2: Environment, Timeout, Process, AnyOf/AllOf conditions, interrupts, Resource, PriorityResource, PreemptiveResource, Container, Store, time-weighted monitoring, and replication-based output analysis, plus bundled safe CLIs. Use when modeling queues, production lines, logistics, or service operations with generator processes; when contending entities need shared resources or preemption; when instrumenting queue length or utilization correctly; when running independent replications with seeds and confidence intervals; when debugging event ordering or run(until=) boundaries. Not for continuous-time ODE or agent-based modeling frameworks.Example requests
Ask your agent in plain language, for example:- “How do I make a SimPy process wait for either a timeout or a resource request, whichever comes first?”
- “My clinic model in clinic_sim.py has 3 nurses as a simpy.Resource and I log len(res.queue) each time a patient arrives, then take the mean. The mean queue length comes out far lower than the M/M/3 formula gives. I also run it with env.run(until=480). What’s wrong, and how do I measure it properly? I’d like a CI over 30 seeds too.”
- “I’m studying a two-stage production line where machines break down and a repair technician has to stop whatever job is running. I need to estimate throughput and how long parts spend in the system, and I want to be able to trust the numbers. How should I build and analyze this?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.