How jprios/queue-model works
This is a production queueing decision-support simulator. In the documented Streamlit workflow, an engineer selects a queue model and configures arrival, service, capacity, and server parameters; the app invokes deterministic simulation or stochastic queue calculations, then presents performance measures and visualizations for congestion, WIP, waiting time, utilization, and losses. A Tkinter interface is also present in the source tree. The excerpts show the model APIs and parts of the Streamlit orchestration; unsampled UI paths are not expanded here.
Main entry points: app.py Streamlit App · Windows.py Tkinter App · Deterministic.py D/D/1/K Simulation · Stochastic.py M/M Queue Models
Components 10
User Interfaces
- Streamlit App
app.py - Tkinter App
Windows.py
Queue Models
- D/D/1/K Simulation
Deterministic.py - M/M Queue Models
Stochastic.py
Analysis Results
- Queue Metrics
Stochastic.py - Simulation Timeline
Deterministic.py - Deterministic Dashboard
app.py - Performance Measures
app.py - Queue Visualizations
Windows.py
Other components
- Production Engineer
Connections 13
- Production Engineer → Streamlit App: configures and runs
- Production Engineer → Tkinter App: configures and runs
- Streamlit App → D/D/1/K Simulation: creates simulation
- Streamlit App → M/M Queue Models: creates model
- D/D/1/K Simulation → Simulation Timeline: computes events
- Streamlit App → Deterministic Dashboard: renders plots
- Streamlit App → Performance Measures: shows results
- M/M Queue Models → Queue Metrics: calculates L, W, ρ
- Streamlit App → Queue Metrics: requests metrics
- Tkinter App → D/D/1/K Simulation: creates simulation
- Tkinter App → M/M Queue Models: creates model
- Tkinter App → Queue Visualizations: dispatches plot actions
- Tkinter App → Queue Metrics: requests metrics