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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.

Este ejercicio forma parte del curso

Supply Chain Analytics in Python

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Instrucciones del ejercicio

Complete the code, near the bottom of the sample code, to create a Pandas DataFrame that shows the slack of the constraints.

Ejercicio interactivo práctico

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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(____)
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