gloop 1.0.0b3

Generalized Linear Object Oriented Programming (GLOOP)

Gloop

Generalized Linear Object Oriented Programming (GLOOP) as a simple pythonic interface for OOP access to PULP. It features simple objects, helpful methods, and additional error checking that simplifies code and streamlines development.

Gloop also happens to be synonymous with the word "pulp" in the English language.

Setup

pip install gloop

Getting Started

gloop is a package designed for object oriented linear programming access to pulp. Technical Docs Here.

Basic Example

import gloop

# Create a variable
my_variable = gloop.Variable(name='my_variable_name', lowBound=0)

# Create a model
my_model = gloop.Model(name="my_model_name", sense="maximize")
# Add an objective for the model
my_model.add_objective(fn=my_variable)
# Add a constraint to the model
my_model.add_constraint(name="my_constraint_name", fn=my_variable <= 5)

# Solve the model
my_model.solve()

# Get the results
# my_model.show_outputs()
#=> {'status': 'Optimal', 'objective': 5.0, 'variables': {'my_variable_name': 5.0}}

Application Example

Blinky

After years of work, your friend Robert, and you launched the brand Blink. Blink's business includes assembling, selling, and distributing a smart pet wearable device, the Blinky22. This device has multiple functions; it monitors pet activity levels, tracks health indicators, provides wellness recommendations, records veterinarian visits, tracks location, and shares this all in real-time through a mobile app. Blinky22 is waterproof and light-weight. The collar is adjustable so that any pet can wear it.

Blinky is manufactured in two assembly plants and it is sold in three regions. Monthly demand per region is shared in Table 1. Currently, assembly plants have no capacity restrictions and can source as many items as needed. Blink’s 3PL carrier charges a transportation cost of (USD)0.12 per unit per mile.

Table 1: Demand in units

Demand	Region 1	Region 2	Region 3
Units per month	2500	4350	3296

Table 2: Distance in Miles

Miles	Region 1	Region 2	Region 3
Assembly Plant 1	105	256	108
Assembly Plant 2	240	136	198

Formulate a model using the available information. Your goal is to minimize the total transportation cost.

# Blink
import gloop


################### DATA ######################
# Transportation data
transport = [
    {'origin_name':'A1', 'destination_name':'R1', 'distance': 105, 'cost_per_mile':0.12,},
    {'origin_name':'A1', 'destination_name':'R2', 'distance': 256, 'cost_per_mile':0.12,},
    {'origin_name':'A1', 'destination_name':'R3', 'distance': 108, 'cost_per_mile':0.12,},
    {'origin_name':'A2', 'destination_name':'R1', 'distance': 240, 'cost_per_mile':0.12,},
    {'origin_name':'A2', 'destination_name':'R2', 'distance': 136, 'cost_per_mile':0.12,},
    {'origin_name':'A2', 'destination_name':'R3', 'distance': 198, 'cost_per_mile':0.12,},
]

# Loop through the transport data to create variable and calculate cost
for t in transport:
    # Create decision variables for each item in transport
    t['amt']=gloop.Variable(name=f"{t['origin_name']}__{t['destination_name']}__amt", lowBound=0)
    # Calculate the variable cost of shipping for each item in tranport
    t['cost']=t['distance']*t['cost_per_mile']


# Demand data
demand = [
    {'name':'R1', 'demand':2500},
    {'name':'R2', 'demand':4350},
    {'name':'R3', 'demand':3296},
]


################### Model #####################
# Initialize the model
my_model = gloop.Model(name="transportation_example", sense='minimize')


# Add the Objective Fn
my_model.add_objective(
    fn=gloop.Sum([t['amt']*t['cost'] for t in transport])
)

# Add Constraints
## Demand Constraint
for d in demand:
    my_model.add_constraint(
        name=f"{d['name']}__demand",
        fn=gloop.Sum([t['amt'] for t in transport if t['destination_name']==d['name']]) >= d['demand'],
    )

# Solve the model
my_model.solve()

################### OUTPUT #####################

# Show the outputs
# my_model.show_outputs() #=>
# {'objective': 145208.16,
#  'status': 'Optimal',
#  'variables': {'A1__R1__amt': 2500.0,
#                'A1__R2__amt': 0.0,
#                'A1__R3__amt': 3296.0,
#                'A2__R1__amt': 0.0,
#                'A2__R2__amt': 4350.0,
#                'A2__R3__amt': 0.0}}