ComeçarComece de graça

Constraint combination exercise

You are working on a distribution plan for a warehouse network. The network has two warehouses (W1, and W2) and each can ship three different types of products (A, B, and C). W1 is small and can either ship 10 products A per a week or 15 products B per a week or 20 products C per a week. You are looking to minimize the total costs.

A Pandas DataFrame named demand is printed in the console and contains the monthly demand for each product. Additionally, the code of the PuLP model to initialize, define decision variables, objective function, and constraint so total shipment of each product equals its demand.

Este exercício faz parte do curso

Supply Chain Analytics in Python

Ver curso

Instruções do exercício

  • Complete the code for the constraint that models the shipping limitations of warehouse W1 for 4 weeks.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Initialize, Define Decision Vars., Objective Function, and Constraints
model = LpProblem("Distribution Planning", LpMinimize)
wh = ['W1','W2']
prod = ['A', 'B', 'C']
X = LpVariable.dicts("ship", [(w, p, c) for c in cust for p in prod for w in wh], 
                     lowBound=0, cat="Integer")
model += lpSum([X[(w, p, c)]*costs.loc[(w, p), c]  for c in cust for p in prod for w in wh])
for c in cust:
    for p in prod:
        model += lpSum([X[(w, p, c)] for w in wh]) == demand.loc[p, c]

# Define Dependent Demand Constraints
model += ((1/10) * lpSum([X[('W1', 'A', c)] for c in cust]) 
          + ____ * lpSum([X[(____, ____, c)] for c in cust])
          + ____ * lpSum([X[(____, ____, ____)] for c in cust])) <= ____
Editar e executar o código