Metadata-Version: 2.3
Name: idinn
Version: 0.1.3
Summary: Inventory dynamics–informed neural networks for solving single-sourcing and dual-sourcing problems.
Project-URL: Homepage, https://gitlab.com/ComputationalScience/idinn
Project-URL: Documentation, https://inventory-optimization.readthedocs.io
Project-URL: Issues, https://gitlab.com/ComputationalScience/idinn/-/issues
Author-email: Jiawei Li <sud.concept0x@icloud.com>, Thomas Asikis <thomas.asikis@uzh.ch>, Ioannis Fragkos <fragkos@rsm.nl>, Lucas Boettcher <l.boettcher@fs.de>
License: MIT License
        
        Copyright (c) 2021 Jiawei Li, Thomas Asikis, Ioannis Fragkos, and Lucas Boettcher
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Keywords: dynamic control,neural networks
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: torch>=2.0
Provides-Extra: dynamic-programming
Requires-Dist: numba; extra == 'dynamic-programming'
Description-Content-Type: text/markdown

# idinn: Inventory-Dynamics Control with Neural Networks

[<img src="https://gitlab.com/ComputationalScience/idinn/-/raw/main/docs/_static/youtube.png" align="center" width="60%" size="auto" alt="youtube">](https://www.youtube.com/watch?v=hUBfTWV6tWQ)

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

## Installation

The package can be installed form 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.controller import SingleSourcingNeuralController

# 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_distribuion="uniform",
    demand_low=1,
    demand_high=4
)
controller = SingleFullyConnectedNeuralController(
    hidden_layers=[2],
    activation=torch.nn.CELU(alpha=1)
)
# Train the neural controller
controller.train(
    sourcing_model=sourcing_model,
    sourcing_periods=50,
    validation_sourcing_periods=1000,
    epochs=5000,
    tensorboard_writer=torch.utils.tensorboard.SummaryWriter(),
    seed=1,
)
# Simulate and plot the results
controller.plot(sourcing_model=sourcing_model, sourcing_periods=100)
# Calculate the optimal order quantity for applications
controller.forward(current_inventory=10, past_orders=[1, 5])
```

## Documentation

For tutorials and documentation, please refer to our [documentation](https://inventory-optimization.readthedocs.io/en/latest/).

## Papers using `idinn`

* Böttcher, 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.

## Contributors

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