idinn

idinn: A Python Package for Inventory-Dynamics Control with Neural Networks - Published in JOSS (2025)

https://gitlab.com/ComputationalScience/idinn

Repository

Inventory dynamics–informed neural networks for solving single-sourcing and dual-sourcing problems.

https://gitlab.com/ComputationalScience/idinn/blob/main/

# idinn: Inventory-Dynamics Control with Neural Networks

[![PyPI Latest Release](https://img.shields.io/pypi/v/idinn.svg)](https://pypi.org/project/idinn/)
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1BAMiveGXmErIp10MK3V_SUJlDAXHAyaI)
[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://idinn-demo.streamlit.app)
[![status](https://joss.theoj.org/papers/224380be40f3be0b741a4ec711eac83b/status.svg)](https://joss.theoj.org/papers/224380be40f3be0b741a4ec711eac83b)

[](https://www.youtube.com/watch?v=hUBfTWV6tWQ)

`idinn` implements **i**nventory **d**ynamics**i**nformed **n**eural **n**etwork and other related controllers for solving single-sourcing and dual-sourcing problems. Neural network controllers and inventory dynamics are implemented into customizable objects using PyTorch as backend to enable users to find the optimal controllers for the user-specified inventory systems.

## Demo

For a quick demo, you can run our [Streamlit app](https://idinn-demo.streamlit.app/). The app allows you to interactively train and evaluate neural controllers for user-specified dual-sourcing systems. Alternatively, you may use our notebook in [Colab](https://colab.research.google.com/drive/1BAMiveGXmErIp10MK3V_SUJlDAXHAyaI).

## Installation

The package can be installed from PyPI. To do that, run

```
pip install idinn
```

Or, if you want to inspect the source code and edit locally, run

```
git clone https://gitlab.com/ComputationalScience/idinn.git
cd idinn
pip install -e .
```

## Example Usage

```python
import torch
from idinn.sourcing_model import SingleSourcingModel
from idinn.single_controller import SingleSourcingNeuralController
from idinn.demand import UniformDemand

# Initialize the sourcing model and the neural controller
sourcing_model = SingleSourcingModel(
    lead_time=0,
    holding_cost=5,
    shortage_cost=495,
    batch_size=32,
    init_inventory=10,
    demand_generator=UniformDemand(low=1, high=4),
)
controller = SingleSourcingNeuralController(
    hidden_layers=[2],
    activation=torch.nn.CELU(alpha=1)
)
# Train the neural controller
controller.fit(
    sourcing_model=sourcing_model,
    sourcing_periods=50,
    validation_sourcing_periods=1000,
    epochs=5000,
    seed=1,
)
# Simulate and plot the results
controller.plot(sourcing_model=sourcing_model, sourcing_periods=100)
# Calculate the optimal order quantity for applications
controller.predict(current_inventory=10)
```

## Documentation

See the official [documentation](https://inventory-optimization.readthedocs.io/en/latest/) for more information.

## Contribution

We welcome contributions of all kinds! To get started, please see our [contribution guide](CONTRIBUTING.md).

## Papers using `idinn`

* Bttcher, Lucas, Thomas Asikis, and Ioannis Fragkos. "Control of dual-sourcing inventory systems using recurrent neural networks." [INFORMS Journal on Computing](https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2022.0136) 35.6 (2023): 1308-1328.
* Li, Jiawei, Thomas Asikis, Ioannis Fragkos, and Bttcher, Lucas. "idinn: A Python package for inventory-dynamics control with neural networks." [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.08508#) 10.112 (2025): 8508.


```
@article{bottcher2023control,
  title={Control of dual-sourcing inventory systems using recurrent neural networks},
  author={B{\"o}ttcher, Lucas and Asikis, Thomas and Fragkos, Ioannis},
  journal={INFORMS Journal on Computing},
  volume={35},
  number={6},
  pages={1308--1328},
  year={2023}
}
```

```
@article{li2025, 
title = {idinn: A {P}ython package for inventory-dynamics control with neural networks}, 
author = {Li, Jiawei and Asikis, Thomas and Fragkos, Ioannis and B{\"o}ttcher, Lucas}, 
journal = {Journal of Open Source Software},
volume = {10}, 
number = {112}, 
pages = {8508}, 
year = {2025}
}
```

## Contributors

* [Jiawei Li](https://github.com/iewaij)
* [Thomas Asikis](https://gitlab.com/asikist)
* [Ioannis Fragkos](https://gitlab.com/ioannis.fragkos1)
* [Lucas Bttcher](https://gitlab.com/lucasboettcher)

Owner

JOSS Publication

idinn: A Python Package for Inventory-Dynamics Control with Neural Networks

Published

August 21, 2025

DOI

10.21105/joss.08508

Volume 10, Issue 112, Page 8508

Authors

Jiawei Li
Department of Computational Science and Philosophy, Frankfurt School of Finance and Management

Thomas Asikis
Game Theory, University of Zurich

Ioannis Fragkos
Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University Rotterdam

Lucas Böttcher
Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, Laboratory for Systems Medicine, Department of Medicine, University of Florida

Editor

Evan Spotte-Smith

Tags

PyTorch artificial neural networks inventory dynamics optimization control dynamic programming

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