JijZept SDK

import jijmodeling as jm

# Define the knapsack problem
v = jm.Placeholder("v", ndim=1)  # Item values
w = jm.Placeholder("w", ndim=1)  # Item weights
W = jm.Placeholder("W")  # Knapsack capacity
N = v.len_at(0, latex="N")  # Number of items
x = jm.BinaryVar("x", shape=(N,))  # Decision variables
i = jm.Element("i", belong_to=(0, N))

problem = jm.Problem("Knapsack", sense=jm.ProblemSense.MAXIMIZE)
problem += jm.sum(i, v[i] * x[i])  # Objective: maximize value
problem += jm.Constraint("weight", jm.sum(i, w[i] * x[i]) <= W)  # Weight constraint

# Instance data
instance_data = {
    "v": [10, 13, 18, 31, 7, 15],  # Item values
    "w": [11, 15, 20, 35, 10, 33],  # Item weights
    "W": 47,  # Knapsack capacity
}

# Create OMMX instance
interpreter = jm.Interpreter(instance_data)
instance = interpreter.eval_problem(problem)

# Value of the objective function
print(f"Value of the objective function: {solution.objective}")

dv_df = solution.decision_variables_df

# Drop unnecessary columns for display
dv_df.drop(columns=["description", "substituted_value"])