Logical constraint exercise
Your customer has ordered six products to be delivered over the next month. You will need to ship multiple truck loads to deliver all of the products. There is a weight limit on your trucks of 25,000 lbs. For cash flow reasons you desire to ship the most profitable combination of products that can fit on your truck.
| Product | Weight (lbs) | Profitability ($US) |
|---|---|---|
| A | 12,583 | 102,564 |
| B | 9,204 | 130,043 |
| C | 12,611 | 127,648 |
| D | 12,131 | 155,058 |
| E | 12,889 | 238,846 |
| F | 11,529 | 197,030 |
Two Python dictionaries weight, and prof, and a list prod have been created for you containing the weight, profitability, and name of each product. You can explore them in the console.
Cet exercice fait partie du cours
Supply Chain Analytics in Python
Instructions
- Add a constraint to ensure the total weight of the truck is less than or equal to 25,000 lbs.
- Add a constraint so that the model will, at most, select only one of the products between D, E, and F.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Initialized model, defined decision variables and objective
model = LpProblem("Loading Truck Problem", LpMaximize)
x = LpVariable.dicts('ship_', prod, cat='Binary')
model += lpSum([prof[i] * x[i] for i in prod])
# Define Constraint
model += lpSum([weight[i] * x[i] for i in prod]) ____ ____
model += ____
model.solve()
for i in prod:
print("{} status {}".format(i, x[i].varValue))