LoslegenKostenlos starten

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.

Diese Übung ist Teil des Kurses

<Kurs>Supply Chain Analytics in Python</Kurs>
Kurs ansehen

Übungsanweisungen

  • Initialize the class.
  • Define the decision variables using LpVariable.dicts and python's list comprehension.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# 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=____)
Code bearbeiten und ausführen