Solving the knapsack problem with Python and Gurobi

During my PyCon presentation on Mathematical optimisation, I used the example of the knapsack problem to walk people through the structure of a linear program. I’ve had a number of people request access to this code, so I thought I’d write it up in a post so it could be more easily shared. This will be a relatively concise article so feel free to refer to the presentation for any additional context.

What is the knapsack problem?

• You have a knapsack that can only carry so much weight (in our case 9 kg)
• Every item you can place in the knapsack has a weight and a value
• You want to maximise the value of the items you choose to put in the knapsack
• What is the most valuable mix of items you can choose?

Why use this example?

I decided to use this particular example because I wanted to illustrate how mathematical optimisation is perfect for solving problems where you have a large number of granular decisions to make whilst subject to constraints. Many industries face some version of these problems. While machine learning is fantastic for prediction/classification problems, mathematical optimisation was developed specifically to solve complex decision problems.

Let’s solve it in Python

Step 1: Import libraries and initialise a model object.

import gurobipy as gp
from gurobipy import GRB

m = gp.Model("knapsack")

Step 2: Create the model decision variables. These are the binary decisions (0 or 1) that determine whether we are selecting this item to go into the knapsack.

gold = m.addVar(vtype=GRB.BINARY, name="gold")
diamond = m.addVar(vtype=GRB.BINARY, name="diamond")
coin = m.addVar(vtype=GRB.BINARY, name="coin")
statue = m.addVar(vtype=GRB.BINARY, name="statue")
brick = m.addVar(vtype=GRB.BINARY, name="brick")
feather = m.addVar(vtype=GRB.BINARY, name="feather")
ark = m.addVar(vtype=GRB.BINARY, name="ark")
book = m.addVar(vtype=GRB.BINARY, name="book")
necklace = m.addVar(vtype=GRB.BINARY, name="necklace")

Step 3: Create the weight constraint – in this case the backpack cannot hold more than 9 kg of objects.

backpack_max_weight = 9
m.addConstr(2 * gold +
     0.2 * diamond +
     0.3 * coin +
     6 * statue +
     2 * brick +
     0.1 * feather +
     15 * ark +
     1 * book +
     0.5 * necklace <= backpack_max_weight, 
     name="max_weight")

Step 4: Create the objective function of the model – we want to maximise the value of the objects we select.

m.setObjective(
    5000 * gold + 
    1500 * diamond + 
    2000 * coin + 
    8000 * statue + 
    3 * brick + 
    1 * feather + 
    1000000 * ark +
    2000 * book +
    3500 * necklace,
    GRB.MAXIMIZE)

Step 5: Run the model and extract the variables and their optimal values.

m.optimize()

for v in m.getVars():
    print(v.VarName, v.X)