Shadow price and slack exercise pt2
In this exercise you are working on the production plan for a company over the next 4 months. Your goal is to determine how much should be produced to minimize the production (fixed + variable), and storage costs while meeting the customers demand. The are constraints on the production capacity and demand each month.
Questo esercizio fa parte del corso
Supply Chain Analytics in Python
Istruzioni dell'esercizio
Complete the code, near the bottom of the sample code, to create a Pandas DataFrame that shows the slack of the constraints.
esercizio interattivo pratico
Prova questo esercizio completando questo codice di esempio.
model = LpProblem("Production Planning", LpMinimize)
time = [1, 2, 3, 4]
s = LpVariable.dicts("stock_in", [0, 1, 2, 3, 4], lowBound=0, cat="Integer")
x = LpVariable.dicts("prod_in", time, lowBound=0, cat="Integer")
y = LpVariable.dicts("plant_on_", time, lowBound=0, cat="Binary")
model += lpSum([d.loc[t,"unit_prod"]*x[t] + d.loc[t,"unit_inv"]*s[t]
+ d.loc[t,"fixed_setup"]*y[t] for t in time])
s[0] = 100
for t in time:
model += s[t-1] + x[t] == d.loc[t,"demand"] + s[t]
model += x[t] <= d.loc[t,"prod_cap"]*y[t]
model.solve()
# Print the Constraint Slack
o = [{'name':name, 'slack':____}
for ____, c in ____]
print(____)