This example solves the cutting stock problem (sometimes also called the cutting rod problem) using a column-generation technique. It is based on https://doi.org/10.5281/zenodo.3329388.
Intuitively, this problem is about cutting large rolls of paper into smaller pieces. There is an exact demand of pieces to meet, and all rolls have the same size. The goal is to meet the demand while maximising the profits (each paper roll has a fixed cost, each sold piece allows earning some money), which is roughly equivalent to using the smallest amount of rolls to cut (or, equivalently, to minimise the amount of paper waste).
This function takes five parameters:
maxwidth: the maximum width of a roll (or length of a rod)
widths: an array of the requested widths
rollcost: the cost of a complete roll
demand: the demand, in number of pieces, for each width
prices: the selling price for each width
Mathematically, this problem might be formulated with two variables:
x[i, j] ∈ ℕ: the number of times the width
iis cut out of the roll
y[j] ∈ 𝔹: whether the roll
Several constraints are needed:
- the demand must be satisfied, for each width
i: ∑j x[i, j] = demand[i]
- the roll size cannot be exceed, for each roll
jthat is used: ∑i x[i, j] width[i] ≤ maxwidth y[j]
If you want to implement this naïve model, you will need an upper bound on the number of rolls to use: the simplest one is to consider that each required width is cut from its own roll, i.e.
j varies from 1 to ∑i demand[i].
This example prefers a more advanced technique to solve this problem: column generation.
It considers a different set of variables: patterns of width to cut a roll. The decisions then become the number of times each pattern is used (i.e. the number of rolls that are cut following this pattern).
The intelligence comes from the way these patterns are chosen: not all of them are considered, but only the "interesting" ones, within the master problem.
A "pricing" problem is used to decide whether a new pattern should be generated or not (it is implemented in the function
solve_pricing). "Interesting" means, for a pattern, that the optimal solution may use this cutting pattern.
In more detail, the solving process is the following. First, a series of dumb patterns are generated (just one width per roll, repeated until the roll is completely cut). Then, the master problem is solved with these first patterns and its dual solution is passed on to the pricing problem. The latter decides if there is a new pattern to include in the formulation or not; if so, it returns it to the master problem. The master is solved again, the new dual variables are given to the pricing problem, until there is no more pattern to generate from the pricing problem: all "interesting" patterns have been generated, and the master can take its optimal decision.
In the implementation, the variables deciding how many times a pattern is chosen are called
For more information on column-generation techniques applied on the cutting stock problem, you can see:
- Integer programming column generation strategies for the cutting stock problem and its variants
- Tackling the cutting stock problem
This example uses the following packages:
using JuMP import GLPK import SparseArrays
solve_pricing implements the pricing problem for the function
It takes, as input, the dual solution from the master problem and the cutting stock instance.
It outputs either a new cutting pattern, or
nothing if no pattern could improve the current cost.
function solve_pricing( dual_demand_satisfaction, maxwidth, widths, rollcost, demand, prices ) reduced_costs = dual_demand_satisfaction + prices n = length(reduced_costs) # The actual pricing model. submodel = Model(GLPK.Optimizer) set_silent(submodel) @variable(submodel, xs[1:n] >= 0, Int) @constraint(submodel, sum(xs .* widths) <= maxwidth) @objective(submodel, Max, sum(xs .* reduced_costs)) optimize!(submodel) new_pattern = round.(Int, value.(xs)) net_cost = rollcost - sum(new_pattern .* (dual_demand_satisfaction .+ prices)) # If the net cost of this new pattern is nonnegative, no more patterns to add. return net_cost >= 0 ? nothing : new_pattern end function example_cutting_stock(; max_gen_cols::Int = 5_000) maxwidth = 100.0 rollcost = 500.0 prices = [ 167.0, 197.0, 281.0, 212.0, 225.0, 111.0, 93.0, 129.0, 108.0, 106.0, 55.0, 85.0, 66.0, 44.0, 47.0, 15.0, 24.0, 13.0, 16.0, 14.0, ] widths = [ 75.0, 75.0, 75.0, 75.0, 75.0, 53.8, 53.0, 51.0, 50.2, 32.2, 30.8, 29.8, 20.1, 16.2, 14.5, 11.0, 8.6, 8.2, 6.6, 5.1, ] demand = [ 38, 44, 30, 41, 36, 33, 36, 41, 35, 37, 44, 49, 37, 36, 42, 33, 47, 35, 49, 42, ] nwidths = length(prices) n = length(widths) ncols = length(widths) # Initial set of patterns (stored in a sparse matrix: a pattern won't # include many different cuts). patterns = SparseArrays.spzeros(UInt16, n, ncols) for i = 1:n patterns[i, i] = min( floor(Int, maxwidth / widths[i]), round(Int, demand[i]) ) end # Write the master problem with this "reduced" set of patterns. # Not yet integer variables: otherwise, the dual values may make no sense # (actually, GLPK will yell at you if you're trying to get duals for # integer problems). m = Model(GLPK.Optimizer) set_silent(m) @variable(m, θ[1:ncols] >= 0) @objective( m, Min, sum( θ[p] * (rollcost - sum(patterns[j, p] * prices[j] for j = 1:n)) for p = 1:ncols ) ) @constraint( m, demand_satisfaction[j=1:n], sum(patterns[j, p] * θ[p] for p = 1:ncols) >= demand[j] ) # First solve of the master problem. optimize!(m) if termination_status(m) != MOI.OPTIMAL warn("Master not optimal ($ncols patterns so far)") end # Then, generate new patterns, based on the dual information. while ncols - n <= max_gen_cols ## Generate at most max_gen_cols columns. if ! has_duals(m) break end new_pattern = solve_pricing( dual.(demand_satisfaction), maxwidth, widths, rollcost, demand, prices, ) # No new pattern to add to the formulation: done! if new_pattern === nothing break end # Otherwise, add the new pattern to the master problem, recompute the # duals, and go on waltzing one more time with the pricing problem. ncols += 1 patterns = hcat(patterns, new_pattern) # One new variable. new_var = @variable(m, [ncols], base_name = "θ", lower_bound = 0) push!(θ, new_var[ncols]) # Update the objective function. set_objective_coefficient( m, θ[ncols], rollcost - sum(patterns[j, ncols] * prices[j] for j = 1:n) ) # Update the constraint number j if the new pattern impacts this production. for j = 1:n if new_pattern[j] > 0 set_normalized_coefficient( demand_satisfaction[j], new_var[ncols], new_pattern[j] ) end end # Solve the new master problem to update the dual variables. optimize!(m) if termination_status(m) != MOI.OPTIMAL @warn("Master not optimal ($ncols patterns so far)") end end # Finally, impose the master variables to be integer and resolve. # To be exact, at each node in the branch-and-bound tree, we would need to # restart the column generation process (just in case a new column would be # interesting to add). This way, we only get an upper bound (a feasible # solution). set_integer.(θ) optimize!(m) if termination_status(m) != MOI.OPTIMAL @warn("Final master not optimal ($ncols patterns)") return end println("Final solution:") for i = 1:length(θ) if value(θ[i]) > 0.5 println("$(round(Int, value(θ[i]))) units of pattern $(i)") end end return end example_cutting_stock()
Final solution: 15 units of pattern 21 26 units of pattern 22 10 units of pattern 27 33 units of pattern 28 30 units of pattern 29 44 units of pattern 30 30 units of pattern 31 26 units of pattern 32 7 units of pattern 35 23 units of pattern 36 1 units of pattern 39 34 units of pattern 41 23 units of pattern 42 2 units of pattern 43 13 units of pattern 44 9 units of pattern 45 5 units of pattern 46 3 units of pattern 47
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