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Objective function 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). Two python lists loc, and size have also been created, containing the different locations, and the two types of plant capacities. All these variables have been printed to the console for your viewing. The code to initialize, and defined the decision variables has been completed for you.

Questo esercizio fa parte del corso

Supply Chain Analytics in Python

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Esercizio pratico interattivo

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# Initialize, and Define Decision Vars.
model = LpProblem("Capacitated Plant Location Model", LpMinimize)
loc = ['USA', 'Germany', 'Japan', 'Brazil', 'India']
size = ['Low_Cap','High_Cap']
x = LpVariable.dicts("production_", [(i,j) for i in loc for j in loc],
                     lowBound=0, upBound=None, cat='Continuous')
y = LpVariable.dicts("plant_", 
                     [(i,s) for s in size for i in loc], cat='Binary')

# Define objective function
model += (lpSum([fix_cost.loc[____,____] * ____[(____,____)] 
                 for s in ____ for i in ____])
          )
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