Metadata-Version: 2.4
Name: supplynetpy
Version: 0.1.3
Summary: Python Library for Modeling and Discrete Event Simulation of Supply Chains
Project-URL: Homepage, https://supplychainsimulation.github.io/SupplyNetPy/
Author-email: Tushar Lone <tusharlone28@gmail.com>, Neha Karanjkar <nehak@iitgoa.ac.in>
Maintainer-email: Tushar Lone <tusharlone28@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Tushar Lone, Neha Karanjkar, Lekshmi P.
        
        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
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        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: discrete event simulation,inventory,modeling,simulation,supply chain
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Requires-Dist: simpy
Description-Content-Type: text/markdown

# SupplyNetPy

SupplyNetPy is a Python library designed for modeling, simulation, design exploration, and optimization of supply chains and inventory systems. It allows users to create and simulate supply chain networks with various inventory replenishment policies.

## Installation

You can install SupplyNetPy using pip:

```sh
pip install supplynetpy
```

## Dependencies

[SimPy](https://simpy.readthedocs.io/en/latest/)

## Authors

- Tushar Lone [GitHub](https://github.com/tusharlone)
- Lekshmi P [GitHub](https://github.com/LekshmiPremkumar)
- Neha Karanjkar [GitHub](https://github.com/NehaKaranjkar)

## Quick Start
#### Creating supply chain networks
~~~
# import the library
import SupplyNetPy.Components as scm

# import simpy and create enviornment
import simpy
env = simpy.Environment()

# let us define a supplier with infinite supply
supplier1 = {'ID': 'S1', 'name': 'Supplier1', 'node_type': 'infinite_supplier'}

# a distributor with inventory
distributor1 = {'ID': 'D1', 'name': 'Distributor1', 'node_type': 'distributor', 
                'capacity': 150, 'initial_level': 50, 'inventory_holding_cost': 0.2,  # inventory params
                'replenishment_policy': scm.SSReplenishment, 'policy_param': {'s':100,'S':150}, # inventory params
                'product_buy_price': 100,'product_sell_price': 105}

# linking supplier1 with distributor1
link1 = {'ID': 'L1', 'source': 'S1', 'sink': 'D1', 'cost': 5, 'lead_time': lambda: 2}

# define demand at the distributor
demand1 = {'ID': 'd1', 'name': 'Demand1', 'order_arrival_model': lambda: 1,
            'order_quantity_model': lambda: 10, 'demand_node': 'D1'}

# create a supply chain network
supplychainnet = scm.create_sc_net(nodes=[supplier1, distributor1], links=[link1], demands=[demand1])

# simulate and see results
supplychainnet = scm.simulate_sc_net(supplychainnet, sim_time=20, logging=True)
~~~


## Documentation
For detailed documentation and advanced usage, please refer to the [official documentation](https://supplychainsimulation.github.io/SupplyNetPy/).