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Shadow price and slack exercise pt1

You are planning what cupcakes a bakery should make. The bakery can make either:

  • regular size cupcake: profit = $5
  • a jumbo cupcake twice the regular size: profit = $10

There are 2 constraints on oven hours (30) and worker hours (65). This scenario has been modeled in PuLP for you and a solution found. The model status, decision variables values, objective value (i.e. profit), the shadow prices and slack of the constraints have been printed in the shell.

The sample script is a copy of that code. You will adjust the constraints to see how the optimal solution changes.

Diese Übung ist Teil des Kurses

<Kurs>Supply Chain Analytics in Python</Kurs>
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Interaktive praktische Übung

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

# Define Constraints, Solve, Print Status, Variables, Objective
model = LpProblem("Maximize Bakery Profits", LpMaximize)
R = LpVariable('Regular_production', lowBound=0, cat='Continuous')
J = LpVariable('Jumbo_production', lowBound=0, cat='Continuous')
model += 5 * R + 10 * J, "Profit"

# Adjust the constraint
model += 0.5 * R + 1 * J <= ____
model += 1 * R + 2.5 * J <= 65

# Solve Model, Print Status, Variables, Objective, Shadow and Slack
model.solve()
print("Model Status: {}".format(LpStatus[model.status]))
for v in model.variables():
    print(v.name, "=", v.varValue)
print("Objective = $", value(model.objective))
o = [{'name':name, 'shadow price':c.pi, 'slack': c.slack} 
     for name, c in model.constraints.items()]
print(pd.DataFrame(o))
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