idinn 0.1.5

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

idinn: Inventory-Dynamics Control with Neural Networks

idinn implements inventory dynamics–informed neural networks 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

import torch
from idinn.sourcing_model import SingleSourcingModel
from idinn.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.forward(current_inventory=10, past_orders=[1, 5])

Documentation

For tutorials and documentation, please refer to our documentation.

Papers using idinn

• Böttcher, Lucas, Thomas Asikis, and Ioannis Fragkos. "Control of Dual-Sourcing Inventory Systems Using Recurrent Neural Networks." INFORMS Journal on Computing 35.6 (2023): 1308-1328.

Contributors

• Jiawei Li
• Thomas Asikis
• Ioannis Fragkos
• Lucas Böttcher