optimal_cuts.py

import pulp, csv


# Conversion factors
in_to_mm = 25.4 # 1 inch is 25.4 mm

# Define the stock boards as a list of tuples (description, length in mm, width in mm, cost)
# All lengths are converted to mm (1 ft = 12 in)
# Conversion factors
in_to_mm = 25.4 # 1 inch = 25.4 millimeters
ft_to_mm = 304.8 # 1 foot = 304.8 millimeters

# Stock boards list
stock_boards = [
    ("Board1", 8 * ft_to_mm, 4 * in_to_mm, 8.37),
    ("Board2", 4 * ft_to_mm, 12 * in_to_mm, 12.56),
    ("Board3", 8 * ft_to_mm, 8 * in_to_mm, 16.75),
    ("Board4", 8 * ft_to_mm, 5 * in_to_mm, 14.22),
    ("Board5", 8 * ft_to_mm, 4 * in_to_mm, 15.34),
    ("Board6", 16 * ft_to_mm, 5 * in_to_mm, 28.32),
    ("Board7", 8 * ft_to_mm, 2 * in_to_mm, 7.68),
    ("Board8", 8 * ft_to_mm, 12 * in_to_mm, 47.38),
    ("Board9", 8 * ft_to_mm, 8 * in_to_mm, 31.64),
    ("Board10", 8 * ft_to_mm, 4 * in_to_mm, 11.33)
    # You can continue adding other boards in similar format if needed.
]

# Print out the stock boards to verify
for board in stock_boards:
    print(f"{board[0]}: Length={board[1]} mm, Width={board[2]} mm, Cost=${board[3]}")

# Define the cuts required as a list of tuples (length in mm, width in mm, quantity)
cuts_required = [
    (2215.6, 60, 2),
    (1066.8, 60, 2),
    (2235.6, 60, 10),
]

# Set up the problem
prob = pulp.LpProblem("2D_Stock_Cutting", pulp.LpMinimize)

# Create a variable for each board type indicating how many of that board we use
x_vars = {i: pulp.LpVariable(f"board_use_{i}", lowBound=0, cat=pulp.LpInteger) for i, board in enumerate(stock_boards)}

# Objective function: minimize the total cost of the boards used
prob += pulp.lpSum([x_vars[i] * board[3] for i, board in enumerate(stock_boards)])

# Add constraints for each type of cut required
for cut in cuts_required:
    length_required, width_required, quantity_required = cut
    prob += (pulp.lpSum([(board[1] // length_required) * (board[2] // width_required) * x_vars[i] for i, board in enumerate(stock_boards)]) >= quantity_required)

# Solve the problem
prob.solve()

# Prepare the data for CSV output
output_data = []
if pulp.LpStatus[prob.status] == 'Optimal':
    print("Optimal solution found:")
    output_data.append(["Board Combination", "Quantity Used", "Total Cost (USD)"])
    for i, board in enumerate(stock_boards):
        if x_vars[i].value() > 0:
            output_line = [board[0], x_vars[i].value(), board[3] * x_vars[i].value()]
            output_data.append(output_line)
            print(f"Use {x_vars[i].value()} pieces of {board[0]} at a total cost of {board[3] * x_vars[i].value()} USD")
    total_cost_line = ["Total", "", pulp.value(prob.objective)]
    output_data.append(total_cost_line)
    print(f"Total cost for this cutting plan: {pulp.value(prob.objective)} USD")
else:
    print("No optimal solution found. Adjust the problem constraints and retry.")

# Write to CSV file
with open('output.csv', mode='w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(output_data)