scx 1.0.3

MIT Supply Chain Python Package

SCx

MIT's Supply Chain Micromaster (SCx) Python Package

Documentation

Technical documentation can be found here.

Local Setup

Make sure you have Python 3.6.x (or higher) installed on your system. You can download it here.

• Recommended (but Optional) - Setup and activate a virtual environment:
  • Install (or upgrade) virtualenv:

  python3 -m pip install --upgrade virtualenv
  

  • Create your virtualenv named venv:

  python3 -m virtualenv venv
  

  • Activate your virtual environment
    • On Unix (Mac or Linux):

    source venv/bin/activate
    

    • On Windows:

    venv\scripts\activate
    

• Then in your terminal:
  • pip install scx

Cloud Setup

Create a google account and access google colab here.

• Create a new notebook
• Install the scx package by adding the following to a new cell and running it:
  • pip install scx

Getting Started

Basic Usage

from scx.optimize import Model

Examples

Simple Optimization

from scx.optimize import Model

# Create variables
product_1_amt = Model.variable(name="product_1", lowBound=0)
product_2_amt = Model.variable(name="product_2", lowBound=0)

# Initialize the model
model = Model(name="Generic_Problem", sense='maximize')

# Add the Objective Fn
model.add_objective(
    fn = (product_1_amt*1)+(product_2_amt*1)
)

# Add Constraints
model.add_constraint(
    name = 'input_1_constraint',
    fn = product_1_amt*1+product_2_amt*2 <= 100
)
model.add_constraint(
    name = 'input_2_constraint',
    fn = product_1_amt*3+product_2_amt*1 <= 200
)

# Solve the model
model.solve(get_duals=True, get_slacks=True)

# Show the outputs
print(model.outputs)

Outputs:

{'status': 'Optimal', 'objective': 80.0, 'variables': {'product_1': 60.0, 'product_2': 20.0}, 'duals': {'input_1_constraint': 0.4, 'input_2_constraint': 0.2}, 'slacks': {'input_1_constraint': -0.0, 'input_2_constraint': -0.0}}

Array Based Optimization

from scx.optimize import Model

# Create variables
data=[
    {
        'name': 'product_1',
        'input_1': 1,
        'input_2': 3,
        'profit': 1,
        'amt': Model.variable(name="product_1", lowBound=0)
    },
    {
        'name': 'product_2',
        'input_1': 2,
        'input_2': 1,
        'profit': 1,
        'amt': Model.variable(name="product_2", lowBound=0)
    }
]

constraints = [
    {
        'name':'input_1',
        'max_amt':100
    },
    {
        'name':'input_2',
        'max_amt':200
    }
]

# Initialize the model
model = Model(name="Array_Problem", sense='maximize')


# Add the Objective Fn
model.add_objective(
    fn=Model.sum([i['amt']*i['profit'] for i in data])
)

# Add Constraints
for j in constraints:
    model.add_constraint(
        name=f'{j["name"]}_constraint',
        fn=Model.sum([i['amt']*i[j['name']] for i in data]) <= j['max_amt']
    )

# Solve the model
model.solve(get_duals=True, get_slacks=True)

# Show the outputs
print(model.outputs)

Outputs:

{'status': 'Optimal', 'objective': 80.0, 'variables': {'product_1': 60.0, 'product_2': 20.0}, 'duals': {'input_1_constraint': 0.4, 'input_2_constraint': 0.2}, 'slacks': {'input_1_constraint': -0.0, 'input_2_constraint': -0.0}}