https://github.com/joaoflavioufmg/desk
Discrete Event Simulation Kit
https://github.com/joaoflavioufmg/desk
discrete-event-simulation experiment-design factorial-analysis open-source simpy
Last synced: about 2 months ago
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Discrete Event Simulation Kit
- Host: GitHub
- URL: https://github.com/joaoflavioufmg/desk
- Owner: joaoflavioufmg
- License: gpl-3.0
- Created: 2025-10-12T20:28:45.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2026-03-10T22:34:56.000Z (3 months ago)
- Last Synced: 2026-03-18T08:51:00.653Z (3 months ago)
- Topics: discrete-event-simulation, experiment-design, factorial-analysis, open-source, simpy
- Language: HTML
- Homepage: https://desk-sim.readthedocs.io/en/latest/
- Size: 77.6 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# DESK β Discrete Event Simulation Kit
[](https://doi.org/10.5281/zenodo.18088013)
[](LICENSE)
[](https://desk-sim.readthedocs.io/en/latest/?badge=latest)
A comprehensive Python framework for **Discrete Event Simulation** [1,2] with advanced analysis, visualization, and experimental design capabilities.
---
## π Overview
**DESK (Discrete Event Simulation Kit)** is a professional-grade simulation framework built on top of **SimPy** [3], designed for modeling complex systems such as hospitals, call centers, manufacturing, and service operations.
DESK addresses the gap of structured experimental design [4] and replication automation in discrete-event simulation [1,2] workflows.
The open-source framework [5] emphasizes:
- modularity,
- reusability,
- transparency (visualization and event logs),
- and statistical analysis.
DESK is suitable for **applied decision support, teaching, and research**.
---
## β¨ Key Features
### Core Simulation Engine
- **Modular Block Architecture**: Reusable building blocks (`CREATE`, `PROCESS`, `DECIDE`, `DISPOSE`)
- **Advanced Resource Management**: Regular, priority-based, and preemptive resources
- **Entity Attributes & State Variables**: Dynamic assignment and modification
- **Priority Scheduling**: Activity-level and entity-level priority control
- **Event Tracing**: Event logging with filtering and replay
- **Visualization**: Graphical interface synchronized with event log printing
---
### Input Analysis (`desk-distfit`)
- **DistFit Tool**: Automated distribution fitting with statistical tests (`desk-distfit`)
- Supports 9+ distributions:
- uniform, triangular, exponential, normal, lognormal, beta, gamma, Weibull
- KolmogorovβSmirnov goodness-of-fit tests
- Multiple output formats: table, JSON, CSV
See [DESK Distribution Fitting Tool](#desk-distribution-fitting-tool-desk-distfit) for further details.
---
### Experimental Design & Analysis
- **Stability Analysis**: Preliminary capacity analysis on utilization (Ο < 1)
- **Littleβs Law verification**: Automatic analysis on stability (**L = Ξ»W**). *The average number of items in the system (L) is the average arrival rate (Ξ») multiplied by the average time an item spends in the system (W)*
- **Warm-Up Analysis**: Automated transient detection
- **Replication Framework**: (Simulation) Automated multi-run experiments with confidence intervals
- **Factorial Experiments**: Full factorial design with interaction analysis
---
### Performance Metrics
- **Entity Metrics**: System time, queue time, service time
- **Resource Metrics**: Utilization, queue length, busy/idle time
- **WIP Tracking**: Time-weighted work-in-process analysis
- **Financial Analysis**: Cost and revenue per activity
---
### Visualization & Reporting
- **Real-Time Visualization**: Process animation during simulation
- **Statistical Plots**:
- Resource utilization over time
- WIP evolution
- System time distributions
- Activity-level metrics
- **BupaR Integration**: Process mining and animation files for ProcessAnimate in R ([processanimateR](https://bupaverse.github.io/processanimateR/)).
- **Automated Reports**: Simulation results with diagnostics and recommendations
---
## π Quick Start
### Installation
```bash
# Clone the repository
git clone https://github.com/joaoflavioufmg/desk.git
cd desk
# Install dependencies
pip install .
# or
pip install -e .
# Then test:
desk-sim -h
desk-sim -m examples/hospital.py --mode visualization
desk-distfit -h
desk-distfit -d input_data/data10.txt
```
# DESK β Discrete Event Simulation Kit
## π Basic Example
Patients arrive at a hospital emergency department, where they are assessed through a triage system and then directed to the appropriate hospital units. The conceptual model is presented* below.
---
**DESK adopts BPMN (.bpmn) as an open, tool-independent notation for representing activity-cycle and process-interaction models. Although BPMN is not a simulation-native language, its standardized semantics and widespread support make it a suitable representation for discrete-event simulation models.*
Models in `.bpmn` format can be created and shared using the
[BPMN Web Modeler (bpmn.io)](https://demo.bpmn.io/).
---
---
(A single replication)
Create a `basic.py` file and Copy/Paste the Part1 code below.
Following, run: `desk-sim -m basic.py --mode single`
```python
# ==============================================================
# Part 1: Basic simulation model: Nurses on emergency triage (hospital)
# ==============================================================
def build_model(until=None, event_logger=None, verbose=True):
import random
from desk.core.simulation_model import SimulationModel
from desk.core.entity import EventLogger
from desk.blocks.create_block import CreateBlock
from desk.blocks.process_block import ProcessBlock
from desk.blocks.dispose_block import DisposeBlock
# Create model
model = SimulationModel(verbose=verbose)
# Add resources
nurses = model.add_resource("Nurses", capacity=3)
# Define blocks
arrivals = CreateBlock(
"Arrivals", model.env,
inter_arrival_time=lambda: random.expovariate(1/10),
entity_prefix="Patient",
event_logger=event_logger
)
triage = ProcessBlock(
"Triage", model.env,
resource=nurses,
delay_time=lambda: random.uniform(5, 10),
resource_units=1,
event_logger=event_logger
)
discharge = DisposeBlock("Discharge", model.env, event_logger=event_logger)
# Register blocks
for block in [arrivals, triage, discharge]:
model.add_block(block)
# Connect flow
arrivals.connect_to(triage)
triage.connect_to(discharge)
return model
# Run a simulation replication
def main():
from desk.core.entity import EventLogger
HOURS = 60 # Time conversion factor (base time: minutes)
DAYS = 1440
YEARS = 525600
# Create event logger
event_logger = EventLogger()
model = build_model(event_logger=event_logger, verbose=False)
model.run_simulation(
until=8*HOURS, # 8 hours
warm_up_period=1*HOURS, # 1 hour
seed=123
)
# Report results
from desk.analytics.reporting import SimulationReporter
reporter = SimulationReporter(model)
reporter.print_results()
reporter._print_activity_metrics()
reporter._print_resource_metrics()
reporter._print_entity_counts()
reporter._print_block_statistics()
return model, event_logger
# ===========================================
# Simulation Kit
# ===========================================
# Run a simulation replication
def run_single_replication():
return main()
# Run a full simulation
def run_replications_cli():
run_replications()
# Run a factorial analysis
def run_factorial_cli():
return factorial_analysis()
# Run the simulation with interface
def run_visualization_cli(simulation_time=500):
return run_visualization(build_model, simulation_time=simulation_time)
```
---
## π Simulation Analysis (Replications)
Now, Copy/Paste the Part2 code below and add to the `basic.py` file.
Following, run: `desk-sim -m basic.py --mode replications`
```python
# ... (after) "return model, event_logger"...
# ==============================================================
# Part 2: Additional code: Full simulation (replications framework)
# ==============================================================
# Define simulation function wrapper
def simulation_wrapper(seed=None, until=None, warm_up_period=None):
"""Wrapper function for replication framework."""
from desk.core.entity import EventLogger
event_logger = EventLogger()
# Create a fresh model
model = build_model(until=until, event_logger=event_logger, verbose=False)
model.run_simulation(
validate_resources=False,
until=until,
seed=seed,
warm_up_period=warm_up_period
)
return model
def run_replications():
from desk.stats.replication import ReplicationFramework
replication_framework = ReplicationFramework(
simulation_function=simulation_wrapper,
n_replications=30
)
HOURS = 60 # Time conversion factor (base time: minutes)
DAYS = 1440
YEARS = 525600
replication_framework.run_replications(
base_seed=12345,
until=8*HOURS,
warm_up_period=1*HOURS
)
# Access results
df = replication_framework.get_results_dataframe()
print(df.describe())
# ... keep the original (Simulation Kit) code
# ===========================================
# Simulation Kit
# ===========================================
```
## π§ͺ Experimental Design (Factorial)
Now, Copy/Paste the Part3 code below and add to the `basic.py` file.
Following, run: `desk-sim -m basic.py --mode factorial`
```python
# ... after "print(df.describe())"...
# ==============================================================
# Part 3: Additional code: Factorial experiment
# ==============================================================
def factorial_analysis():
"""Factorial analysis with simulation."""
from desk.stats.factorial import FactorialExperiment
HOURS = 60 # Time conversion factor (base time: minutes)
DAYS = 1440
YEARS = 525600
def simulation_wrapper(arrival_rate=1, num_nurses=1,
seed=None, until=None, warm_up_period=0, **kwargs):
"""Wrapper that adapts parameters for factorial analysis."""
from desk.core.entity import EventLogger
event_logger = EventLogger()
# Create a fresh model
model = build_model(until=until, event_logger=event_logger, verbose=False)
model.run_simulation(
validate_resources=False,
until=until,
seed=seed,
warm_up_period=warm_up_period
)
return model
# Create factorial analysis
factorial = FactorialExperiment(
simulation_function=simulation_wrapper,
base_seed=12345
)
# Add factors
factorial.add_factor(
factor_name='arrival_rate',
parameter_path='CreateBlock.inter_arrival_time',
levels=[1, 2, 3], # Minutes between arrivals
description='Inter arrival rates (min)'
)
factorial.add_factor(
factor_name='num_nurses',
parameter_path='Resource.nurses.capacity',
levels=[1, 2, 3],
description='Number of nurses'
)
# Run experiment
factorial.run_factorial_experiment(
n_replications=5,
simulation_time=4*HOURS, # 4 hours
warm_up_period=1/2*HOURS, # 1/2 hour
verbose=True
)
# Analyze results
factorial.print_summary()
factorial.plot_correlation_matrix()
factorial.plot_main_effects('system_time_avg')
factorial.plot_interaction_effects('system_time_avg', 'arrival_rate', 'num_nurses')
return factorial
# ... keep the original (Simulation Kit) code
# ===========================================
# Simulation Kit
# ===========================================
```
## π Run Interface (Visualization)
Finally, Copy/Paste the Part4 code below and add to the `basic.py` file.
Following, run: `desk-sim -m basic.py --mode visualization`
Actually, this can be the first part in a simulation study.
```python
# ... after "return factorial"...
# ==============================================================
# Part 4: Additional code: Interface - visualization
# ==============================================================
from desk.visualization.interface import run_visualization
# ... keep the original (Simulation Kit) code
# ===========================================
# Simulation Kit
# ===========================================
```
---
## π Project Structure
```text
DESK/
βββ src/ # Source code of DESK
| βββ config/ # Simulation setting
| βββ core/ # Core simulation engine
| βββ blocks/ # Simulation building blocks
| βββ analytics/ # Metrics, plots, reports
| βββ stats/ # Replication & factorial design
| βββ validation/ # Stability and warm-up analysis
| βββ visualization/ # Real-time visualization
| βββ distfit/distfit.py # DistFit CLI tool
βββ input_data/
βββ examples/
βββ 1) hospital.py # Hospital example
βββ 2) 2.py # Restaurant example
βββ 3) 3.py, 3a.py, 3b.py # Call center (and variations 3a, 3b)
βββ README.md
```
---
## π Example Models
1) **Hospital Emergency Department**
Triage, multiple resources, priority routing, financial tracking (`hospital.py`)
2) **Restaurant Service**
Multi-resource activities, dynamic attributes, financials (`2.py`)
3) **Call Center with Lost Calls**
Trunk capacity, blocking, retrials, custom KPIs (`3.py`, `3a.py`, `3b.py`)
---
## π Running examples
For each example, you can run: (1) replication, (2) full simulation, (3) factorial analysis and (4) visualization.
DESK examples are executed **directly from the command line**, using explicit execution modes.
---
### π List available modes
To list the execution modes for a given model, e.g., `hospital.py`, type:
```bash
desk-sim -m examples/hospital.py --list-modes
```
```text
DESK execution modes:
--mode single β run a single replication
--mode replications β run the full simulation
--mode factorial β run a factorial analysis
--mode visualization β run simulation interface
```
---
### π Interactive visualization
Runs the model using the **DESK visualization interface**, enabling interactive inspection of the evolving system.
```bash
desk-sim -m examples/hospital.py --mode visualization
```
### βΆοΈ Running a single replication
Runs **one complete replication run**, with full tracing, reporting, plots, and diagnostics.
```bash
desk-sim -m examples/hospital.py --mode single
```
---
### π Running the full simulation (multiple replications)
Runs **multiple independent replications**, aggregates results, and computes confidence intervals and statistical analysis.
```bash
desk-sim -m examples/hospital.py --mode replications
```
---
### π§ͺ Factorial analysis
Runs a **factorial experiment**, varying model parameters and analyzing main and interaction effects.
```bash
desk-sim -m examples/hospital.py --mode factorial
```
---
## π¬ Validation & Verification
DESK includes:
* Stability checker (utilization Ο < 1)
* Littleβs Law analysis
* Resource consistency validation
* Automated warm-up suggestion
---
## π οΈ Requirements
* Python >= 3.10
* simpy == 4.1.1
* numpy == 2.2.6
* pandas == 2.3.1
* scipy == 1.15.3
* matplotlib == 3.10.5
**Optional (for process mining):**
* R >= 4.0
* BupaR
* processanimateR
The resulting event-log .csv file is stored in result/ folder, e.g., `hospital_event_log.csv` file, to produce the animation below. The R code of the example (`hospital_bupar.R`) is available in r_animation/ folder.
---
---
## DESK Distribution Fitting Tool (Desk-DistFit)
### π Input Analysis with Desk-DistFit
*How were the input models used in the previous example, such as `random.expovariate(1/10)` or `random.uniform(5, 10)`, derived from empirical data?*
Desk DistFit (`desk-distfit`) is a Python tool for fitting probability distributions to empirical data using statistical tests. Inspired by previous works [6,7], this tool helps identify the best-fitting probability distribution from a set of common distributions and provides Python code for generating random numbers from the fitted distribution to DESK models.
Within DESK, `desk-distfit` addresses this question by performing *statistical input analysis*, identifying the probability distribution that best fits observed data and replacing generic assumptions with data-driven, statistically validated simulation inputs.
`desk-distfit` is the official DESK input-analysis CLI for statistically fitting probability distributions to empirical data.
*DESK adopts a verb-oriented command-line interface, where simulation tasks are expressed as structured actions (`desk-distfit`), ensuring consistency, reproducibility, and ease of learning across the framework.*
## Features
- **Multiple Distribution Support**: Tests 9 common probability distributions (uniform, triangular, exponential, normal, lognormal, beta, gamma, Weibull)
- **Statistical Testing**: Uses Kolmogorov-Smirnov test for goodness-of-fit assessment
- **Command-Line Interface**: Easy-to-use CLI with comprehensive options
- **Multiple Output Formats**: Results can be saved as table, CSV, or JSON
- **Visualization**: Generates comparative plots of fitted distributions
- **Python Code Generation**: Automatically generates Python code for the best-fitting distribution
- **Robust Error Handling**: Comprehensive error handling and logging
**Output includes:**
* Goodness-of-fit statistics
* Best-fit distribution
* Parameter estimates
* Ready-to-use Python code for DESK models, such as the texts `random.expovariate(1/10)` or `random.uniform(5, 10)`.
## Usage
### Basic Usage
Fit probability distributions to empirical data:
```bash
# Basic usage
desk-distfit -d input_data/foo.txt
# Custom significance level
desk-distfit -d input_data/foo.txt -a 0.01
# Test specific distributions
desk-distfit -d input_data/foo.txt --distributions norm expon gamma
# Save results
desk-distfit -d input_data/foo.txt -o results.txt --format json
# Skip plotting
desk-distfit -d input_data/foo.txt --no-plot
# Help
desk-distfit -h
```
### Command-Line Options
| Option | Description | Default |
|--------|-------------|---------|
| `-d, --data` | Path to data file (required) | - |
| `-a, --alpha` | Significance level for statistical tests | 0.05 |
| `-b, --bins` | Number of histogram bins | 50 |
| `--distributions` | Specific distributions to test | All |
| `--no-plot` | Skip generating plots | False |
| `--show-all` | Show all distributions in plot | False |
| `-o, --output` | Output file path | None |
| `--format` | Output format (table/csv/json) | table |
| `-v, --verbose` | Enable verbose logging | False |
| `-h, --help` | Show help message | - |
### Examples
```bash
# Basic analysis
desk-distfit -d input_data/foo.txt
# Custom significance level
desk-distfit -d input_data/foo.txt -a 0.01
# Test specific distributions only
desk-distfit -d input_data/foo.txt --distributions norm expon gamma
# Save results to file
desk-distfit -d input_data/foo.txt -o results.txt
# Generate CSV output
desk-distfit -d input_data/foo.txt -o results.csv --format csv
# Skip plotting (useful for batch processing)
desk-distfit -d input_data/foo.txt --no-plot
# Show all fitted distributions in plot
desk-distfit -d input_data/foo.txt --show-all
# Verbose output for debugging
desk-distfit -d input_data/foo.txt -v
# Complete example with multiple options
desk-distfit -d input_data/foo.txt -a 0.01 -b 100 --show-all -o results.json --format json -v
```
## Input Data Format
The input file should contain one numeric value per line:
```
1.234
2.567
0.891
3.456
...
```
**Supported formats:**
- Plain text files (.txt)
- One number per line
- UTF-8 encoding
- Blank lines are ignored
## Supported Distributions
| Distribution | Python Random Function | Parameters |
|-------------|------------------------|------------|
| Uniform | `random.uniform(a, b)` | a, b |
| Triangular | `random.triangular(low, high, mode)` | low, high, mode |
| Exponential | `random.expovariate(lambd)` | lambda |
| Normal | `random.gauss(mu, sigma)` | mu, sigma |
| Log-Normal | `random.lognormvariate(mu, sigma)` | mu, sigma |
| Beta | `random.betavariate(alpha, beta)` | alpha, beta |
| Gamma | `random.gammavariate(alpha, beta)` | alpha, beta |
| Weibull (Min) | `random.weibullvariate(alpha, beta)` | alpha, beta |
| Weibull (Max) | `random.weibullvariate(alpha, beta)` | alpha, beta |
## Output
### Console Output
The tool provides:
1. **Data statistics** (sample size, mean, std dev, min, max)
2. **Distribution fitting results** with p-values and significance indicators
3. **Parameter details** for all fitted distributions
4. **Summary report** with best-fitting distribution
5. **Python code** for generating random numbers for DESK model.
### Example Output
```
Data Statistics:
Sample size: 200
Mean: 2.0156
Std Dev: 2.0298
Min: 0.0089
Max: 11.2445
Item Distribution Statistic P-value Significant
------------------------------------------------------------
1 expon 0.0456 0.8234 (*)
2 gamma 0.0523 0.7891 (*)
3 norm 0.0789 0.4567
...
Distribution Fitting Summary Report
==================================================
Best Fitting Distribution: expon
- Parameters: loc=0.000, scale=2.016
- P-value: 0.8234
- Significant at Ξ±=0.05: Yes
Python Random Code:
random.expovariate(0.496)
```
### File Output Formats
#### Table Format (default)
Human-readable text format with detailed results and parameters.
#### CSV Format
```csv
Distribution,P_value,Statistic,Significant,Python_Code
expon,0.823400,0.045600,Yes,random.expovariate(0.496)
gamma,0.789100,0.052300,Yes,random.gammavariate(1.024,0.496)
...
```
#### JSON Format
```json
{
"summary": {
"sample_size": 200,
"best_distribution": "expon",
"alpha": 0.05
},
"results": [
{
"distribution": "expon",
"p_value": 0.8234,
"statistic": 0.0456,
"parameters": {"loc": 0.0, "scale": 2.016},
"significant": true,
"python_code": "random.expovariate(0.496)"
}
]
}
```
## Interpretation
### P-Values
- **p β₯ Ξ±**: Distribution is a good fit (significant)
- **p < Ξ±**: Distribution is not a good fit (reject)
- Higher p-values indicate better fits
### Significance Indicators
- **(*) asterisk**: Indicates significant fit at the chosen Ξ± level
- Results are sorted by p-value (best fit first)
## Statistical Method
The tool uses the **Kolmogorov-Smirnov test** [8] to assess goodness-of-fit:
1. **Null Hypothesis (Hβ)**: The data follows the tested distribution
2. **Alternative Hypothesis (Hβ)**: The data does not follow the tested distribution
3. **Test Statistic**: Maximum difference between empirical and theoretical CDFs
4. **Decision Rule**: Reject Hβ if p-value < Ξ±
## Limitations
- **Sample Size**: Requires sufficient data points (recommended: n β₯ 30)
- **Distribution Assumptions**: Only tests common continuous distributions
- **Parameter Estimation**: Uses Maximum Likelihood Estimation (MLE)
- **Independence**: Assumes data points are independent
- **Stationarity**: Assumes data comes from a stationary process
### Help (DESK and DESK-DistFit)
```bash
# Show detailed help
desk-sim -h
desk-distfit -h
# Enable verbose output for debugging
desk-distfit -d input_data/foo.txt -v
```
### Development Setup
```bash
# Clone the repository
git clone https://github.com/joaoflavioufmg/desk.git
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install development dependencies
cd desk
pip install .
# Run tests
pytest
```
## References
[1] Banks, J. (2005). Discrete event system simulation. Pearson Education India.
[2] Law, A. M., Kelton, W. D., & Kelton, W. D. (2007). Simulation modeling and analysis (Vol. 3). New York: Mcgraw-hill. ISBN10: 0073401323 | ISBN13: 9780073401324 https://www.mheducation.com/highered/product/simulation-modeling-and-analysis-law.html?viewOption=instructor
[3] Matloff, N. (2008). Introduction to discrete-event simulation and the simpy language. Davis, CA. Dept of Computer Science. University of California at Davis. Retrieved on August, 2(2009), 1-33. https://heather.cs.ucdavis.edu/matloff/public_html/156/PLN/DESimIntro.pdf
[4] Kleijnen, J. P. (2015, September). Design and analysis of simulation experiments. In International workshop on simulation (pp. 3-22). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-76035-3_1
[5] Stodden, Victoria, Marcia McNutt, David H. Bailey, et al. 2016. βEnhancing Reproducibility for Computational Methods.β Science 354 (6317). https://doi.org/10.1126/science.aah6168.
[6] Distribution fitting with Python: https://medium.com/@amirarsalan.rajabi/distribution-fitting-with-python-scipy-bb70a42c0aed
[7] https://fitter.readthedocs.io/en/latest/
[8] Massey Jr, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association, 46(253), 68-78.
## Changelog
### Version 1.1.2
- Complete rewrite DESK CLI interface
- Complete rewrite DESK-DistFit CLI interface
### Version 1.1.0
- Complete rewrite with object-oriented design
- Added command-line interface
- Multiple output formats support
- Enhanced error handling and logging
- Improved visualization
### Version 1.0.0
- Initial release
- Basic distribution fitting functionality
- Simple plotting capabilities
---
## π€ Contributing
Contributions are welcome:
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Open a Pull Request
---
## π License
GPL-3.0 License β see `LICENSE` file.
- The DESK documentation are licensed under Creative Commons
Attribution 4.0 (CC BY 4.0).
---
## π¨βπ« Acknowledgements
**Author:** Prof. JoΓ£o FlΓ‘vio de Freitas Almeida
**Graduate Program:** PPGEP β UFMG (Brazil)
**Course:** Simulating Logistics Systems
**Credits:**
* SimPy (Python)
* bupaR, processAnimate (R)
---
## π Citation
If you use DESK in academic work, please cite:
```bibtex
@software{desk2025,
author = {Almeida, JoΓ£o FlΓ‘vio de Freitas},
title = {DESK: Discrete Event Simulation Kit},
year = {2025},
institution = {PPGEP-UFMG},
url = {https://github.com/joaoflavioufmg/desk}
}
```