Decision variables of case study
Continue the case study of the Capacitated Plant Location model of a car manufacture. You are given four Pandas data frames demand, var_cost, fix_cost, and cap containing the regional demand (thous. of cars), variable production costs (thous. $US), fixed production costs (thous. $US), and production capacity (thous. of cars). All these variables have been printed to the console for your viewing.
Questo esercizio fa parte del corso
Supply Chain Analytics in Python
Istruzioni dell'esercizio
- Initialize the class.
- Define the decision variables using
LpVariable.dictsand python's list comprehension.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Initialize Class
model = LpProblem("Capacitated Plant Location Model", ____)
# Define Decision Variables
loc = ['USA', 'Germany', 'Japan', 'Brazil', 'India']
size = ['Low_Cap','High_Cap']
x = LpVariable.dicts("production_",
[(i,j) for ____ in ____ for ____ in ____],
lowBound=____, upBound=____, cat=_____)
y = LpVariable.dicts("plant_",
[____ for ____ in ____ for ____ in ____], cat=____)