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Or tools vrppd #24

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27cb85e
Added test_vrppd execution to ortools test suite
Sutekinaa Jul 24, 2026
d9dbad8
added support for vrppd
Sutekinaa Sep 9, 2026
bf1e96a
changed comment on obj parsing
Sutekinaa Sep 10, 2026
6166c63
Merge branch 'main' of https://github.com/NexOR-Optimization/MathOptV…
Sutekinaa Sep 16, 2026
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202 changes: 184 additions & 18 deletions ext/MathOptVRPORToolsExt.jl
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Original file line number Diff line number Diff line change
@@ -1,12 +1,17 @@
module MathOptVRPORToolsExt

# Minimal MathOptVRP backend on top of OR-Tools' CP-SAT solver. Scope is
# narrow: just enough to run `MathOptVRP.test_vrp`. We accept one
# `MathOptVRP.Partition` set of variables and a `MOI.ScalarNonlinearFunction`
# objective built from `MathOptVRP.op_sum_distances` (one leaf per truck,
# optionally wrapped in `:+` nodes), then lower the VRP to a CP-SAT
# `CpModelProto` with a `RoutesConstraintProto` and hand it to
# `ORTools.SolveCpModelWithParameters`, the CP-SAT C API.
# Minimal MathOptVRP backend on top of OR-Tools' CP-SAT solver. We accept
# one `MathOptVRP.PartitionPD` set of variables (the most general route
# constructor; `MathOptVRP.Partition` and `MathOptVRP.Permutation` reach us
# bridged into it, see `MathOptVRP.Bridges`) and a
# `MOI.ScalarNonlinearFunction` objective built from
# `MathOptVRP.op_sum_distances` (one leaf per truck, optionally wrapped in
# `:+` nodes), then lower the problem to a CP-SAT `CpModelProto` with a
# `RoutesConstraintProto` and hand it to `ORTools.SolveCpModelWithParameters`,
# the CP-SAT C API. Pickup/delivery pairs (same vehicle + precedence) are not
# natively expressible in `RoutesConstraintProto`, so they are lowered to
# extra per-arc "rank"/"route id" propagation constraints, see
# `_build_cp_model`.

# ORTools has a `CPSATOptimizer` but that we could extend but it still seems to be WIP,
# e.g., no `optimize!` function and https://github.com/google/or-tools/pull/5219
Expand All @@ -27,7 +32,7 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
next_variable::Int
next_constraint::Int
variable_to_position::Dict{MOI.VariableIndex,Tuple{Int,Int}}
partition::Union{Nothing,MathOptVRP.Partition}
partition::Union{Nothing,MathOptVRP.PartitionPD}
objective_sense::MOI.OptimizationSense
objective_function::Union{Nothing,MOI.ScalarNonlinearFunction}
silent::Bool
Expand Down Expand Up @@ -144,14 +149,15 @@ end

# Variables

function MOI.supports_add_constrained_variables(::Optimizer, ::Type{MathOptVRP.Partition})
function MOI.supports_add_constrained_variables(::Optimizer, ::Type{MathOptVRP.PartitionPD})
return true
end

function MOI.add_constrained_variables(m::Optimizer, set::MathOptVRP.Partition)
function MOI.add_constrained_variables(m::Optimizer, set::MathOptVRP.PartitionPD)
m.partition === nothing ||
error("ORTools: only one MathOptVRP.Partition set is supported per model")
n_rows, n_cols = set.num_clients, set.num_trucks
error("ORTools: only one MathOptVRP.PartitionPD set is supported per model")
n_rows = set.num_services + 2 * set.num_pickup_deliveries
n_cols = set.num_trucks
n = n_rows * n_cols
vars = Vector{MOI.VariableIndex}(undef, n)
for k = 1:n
Expand All @@ -164,7 +170,8 @@ function MOI.add_constrained_variables(m::Optimizer, set::MathOptVRP.Partition)
end
m.partition = set
m.next_constraint += 1
ci = MOI.ConstraintIndex{MOI.VectorOfVariables,MathOptVRP.Partition}(m.next_constraint)
ci =
MOI.ConstraintIndex{MOI.VectorOfVariables,MathOptVRP.PartitionPD}(m.next_constraint)
return vars, ci
end

Expand All @@ -175,7 +182,12 @@ function MOI.copy_to(dest::Optimizer, src::MOI.ModelLike)
return MOI.Utilities.default_copy_to(dest, src)
end

# ── Objective parsing (mirrors the Vroom wrapper) ────────────────────
# ── Objective parsing ─────────────────────────────────────────────────
# JuMP produces `sum(op_sum_distances(M, [depot; col; depot]) for i = 1:T)`
# as a `ScalarNonlinearFunction`. The root is either a single
# `:sum_distances` leaf (T == 1) or a tree of `:+` nodes whose leaves are
# all `:sum_distances`. Each leaf's args[1] is the distance matrix and
# args[2] is `[depot, var, var, ..., var, depot]`.

function _collect_sum_distances_leaves!(
leaves::Vector{MOI.ScalarNonlinearFunction},
Expand Down Expand Up @@ -268,7 +280,148 @@ end

_arc_var_index(i::Int, j::Int, n_loc::Int) = i * (n_loc - 1) + (j > i ? j - 1 : j)

function _build_cp_model(M::AbstractMatrix{<:Real}, ext_depot::Int, n_trucks::Int)
# `RoutesConstraintProto` only encodes connectivity (one multi-circuit
# through the depot); it has no notion of pickup/delivery pairing. So a
# pair `(p, d)` (external ids) is lowered to two auxiliary "dimensions",
# propagated one arc at a time via enforced linear constraints:
# - `route[i]`: the internal id of the first customer on `i`'s route,
# used as a route identifier so `route[p] == route[d]` means "same
# vehicle".
# - `rank[i]`: `i`'s 1-based position along its route, so
# `rank[p] < rank[d]` means "visited before".
# Every non-depot node has exactly one incoming arc (the `routes`
# constraint enforces this), so these are well-defined for every node.
function _add_pickup_delivery_constraints!(
constraints::Vector{Sat.ConstraintProto},
variables::Vector{Sat.IntegerVariableProto},
n_loc::Int,
n_arcs::Int,
pd_pairs::Vector{Tuple{Int,Int}},
ext_to_int::Dict{Int,Int},
)
isempty(pd_pairs) && return
n_customers = n_loc - 1
rank_var(i::Int) = Int32(n_arcs + (i - 1))
route_var(i::Int) = Int32(n_arcs + n_customers + (i - 1))
for i = 1:n_customers
push!(variables, Sat.IntegerVariableProto("rank_$i", Int64[1, n_customers]))
end
for i = 1:n_customers
push!(variables, Sat.IntegerVariableProto("route_$i", Int64[1, n_customers]))
end
for i = 0:(n_loc-1), j = 1:(n_loc-1)
i == j && continue
lit = Int32(_arc_var_index(i, j, n_loc))
if i == 0
# rank needs to start at one
push!(
constraints,
Sat.ConstraintProto(
"rank_start_$j",
Int32[lit],
PB.OneOf(
:linear,
Sat.LinearConstraintProto(
Int32[rank_var(j)],
Int64[1],
Int64[1, 1],
),
),
),
)
# route id == internal first customer id
push!(
constraints,
Sat.ConstraintProto(
"route_start_$j",
Int32[lit],
PB.OneOf(
:linear,
Sat.LinearConstraintProto(
Int32[route_var(j)],
Int64[1],
Int64[j, j],
),
),
),
)
else
# precedence constraint
push!(
constraints,
Sat.ConstraintProto(
"rank_step_$(i)_$(j)",
Int32[lit],
PB.OneOf(
:linear,
Sat.LinearConstraintProto(
Int32[rank_var(j), rank_var(i)],
Int64[1, -1],
Int64[1, 1],
),
),
),
)
# identical route constraint
push!(
constraints,
Sat.ConstraintProto(
"route_step_$(i)_$(j)",
Int32[lit],
PB.OneOf(
:linear,
Sat.LinearConstraintProto(
Int32[route_var(j), route_var(i)],
Int64[1, -1],
Int64[0, 0],
),
),
),
)
end
end
for (p_ext, d_ext) in pd_pairs
p, d = ext_to_int[p_ext], ext_to_int[d_ext]
push!(
constraints,
Sat.ConstraintProto(
"same_route_$(p)_$(d)",
Int32[],
PB.OneOf(
:linear,
Sat.LinearConstraintProto(
Int32[route_var(p), route_var(d)],
Int64[1, -1],
Int64[0, 0],
),
),
),
)
push!(
constraints,
Sat.ConstraintProto(
"precedence_$(p)_$(d)",
Int32[],
PB.OneOf(
:linear,
Sat.LinearConstraintProto(
Int32[rank_var(d), rank_var(p)],
Int64[1, -1],
Int64[1, n_customers],
),
),
),
)
end
return
end

function _build_cp_model(
M::AbstractMatrix{<:Real},
ext_depot::Int,
n_trucks::Int,
pd_pairs::Vector{Tuple{Int,Int}} = Tuple{Int,Int}[],
)
n_loc = size(M, 1)
n_loc == size(M, 2) || error("ORTools: distance matrix must be square; got $(size(M))")
int_to_ext = Int[ext_depot]
Expand All @@ -277,6 +430,7 @@ function _build_cp_model(M::AbstractMatrix{<:Real}, ext_depot::Int, n_trucks::In
push!(int_to_ext, ext)
end
@assert length(int_to_ext) == n_loc
ext_to_int = Dict(ext => i - 1 for (i, ext) in enumerate(int_to_ext))

n_arcs = n_loc * (n_loc - 1)
variables = Sat.IntegerVariableProto[
Expand Down Expand Up @@ -317,6 +471,15 @@ function _build_cp_model(M::AbstractMatrix{<:Real}, ext_depot::Int, n_trucks::In
Sat.ConstraintProto("vehicle_limit", Int32[], PB.OneOf(:linear, vehicle_lim)),
)

_add_pickup_delivery_constraints!(
constraints,
variables,
n_loc,
n_arcs,
pd_pairs,
ext_to_int,
)

obj_vars = Int32[]
obj_coeffs = Int64[]
for i = 0:(n_loc-1), j = 0:(n_loc-1)
Expand Down Expand Up @@ -426,7 +589,7 @@ end

function MOI.optimize!(m::Optimizer)
m.partition !== nothing ||
error("ORTools: model has no `MathOptVRP.Partition` variables")
error("ORTools: model has no `MathOptVRP.PartitionPD` variables")
m.objective_function !== nothing && m.objective_sense == MOI.MIN_SENSE ||
error("ORTools: requires a `MIN_SENSE` `:sum_distances` objective")

Expand All @@ -437,7 +600,7 @@ function MOI.optimize!(m::Optimizer)
parsed = [_parse_leaf(m, leaf) for leaf in leaves]
n_trucks = length(leaves)
n_trucks == m.partition.num_trucks || error(
"ORTools: objective has $n_trucks `:sum_distances` terms but Partition has $(m.partition.num_trucks)",
"ORTools: objective has $n_trucks `:sum_distances` terms but PartitionPD has $(m.partition.num_trucks)",
)
M_ref = parsed[1][1]
depot = parsed[1][2]
Expand All @@ -446,7 +609,10 @@ function MOI.optimize!(m::Optimizer)
dep == depot || error("ORTools: per-truck depots must agree")
end

cp_model, int_to_ext = _build_cp_model(M_ref, depot, n_trucks)
ns, npd = m.partition.num_services, m.partition.num_pickup_deliveries
pd_pairs = Tuple{Int,Int}[(ns + k, ns + npd + k) for k = 1:npd]

cp_model, int_to_ext = _build_cp_model(M_ref, depot, n_trucks, pd_pairs)
n_loc = length(int_to_ext)

response = _solve(cp_model, _sat_parameters(m))
Expand Down
8 changes: 6 additions & 2 deletions test/test_ortools.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,10 @@ using ORTools # triggers loading of `MathOptVRPORToolsExt`
import ORTools_jll # provides the OR-Tools binaries to `ORTools`
using Test

@testset "MathOptVRP.test_vrp (ORTools/CP-SAT)" begin
MathOptVRP.Tests.test_vrp(MathOptVRP.ortools_optimizer)
@testset "$test" for test in [
MathOptVRP.Tests.test_tsp,
MathOptVRP.Tests.test_vrp,
MathOptVRP.Tests.test_vrppd,
]
test(MathOptVRP.ortools_optimizer)
end