CommencerCommencer gratuitement

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.

Cet exercice fait partie du cours

Supply Chain Analytics in Python

Afficher le cours

Instructions

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

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

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(____)
Modifier et exécuter le code