LpVariable dictionary function

Supply Chain Analytics in Python

Aaren Stubberfield

Supply Chain Analytics Mgr.

Moving from simple to complex

Complex Bakery Example

# Define Decision Variables
A = LpVariable('A', lowBound=0, cat='Integer')
B = LpVariable('B', lowBound=0, cat='Integer')
C = LpVariable('C', lowBound=0, cat='Integer')
D = LpVariable('D', lowBound=0, cat='Integer')
E = LpVariable('E', lowBound=0, cat='Integer')
F = LpVariable('F', lowBound=0, cat='Integer')

# Define Objective Function
var_dict = {"A":A, "B":B, "C":C, "D":D, "E":E, "F":F}

# Define Objective Function
model += lpSum([profit_by_cake[type] * var_dict[type] for type in cake_types])

Using LpVariable.dicts()

LpVariable(name, indexs, lowBound=None, upBound=None, cat='Continuous')

name = The prefix to the name of each LP variable created
indexs = A list of strings of the keys to the dictionary of LP variables
lowBound = Lower bound
upBound = Upper bound
cat = The type of variable this is
- Integer
- Binary
- Continuous (default)

LpVariable.dicts() with list comprehension

LpVariable.dicts() often used with Python's list comprehension

Transportation Optimization

# Define Decision Variables
customers = ['East','South','Midwest','West']
warehouse = ['New York','Atlanta']
transport = LpVariable.dicts("route", [(w,c) for w in warehouse for c in customers],
                              lowBound=0, cat='Integer')

# Define Objective
model += lpSum([cost[(w,c)]*transport[(w,c)] for w in warehouse for c in customers])

Summary

Creating many LP variables for complex problems
LpVariable.dicts()
Used with list comprehension

Now you try it out

Supply Chain Analytics in Python