Test python 3.12

- Enforce cffi>=1.17 (updated for python 3.12, avoid weird errors for
  dependencies relying on C code)
- Update time limits to avoid errors with ubuntu + python 12:
  It seems ortools gets slower in its latest release 9.11 on ubuntu...
- Fix LP_MRCPSP_GUROBI for python 3.12:
  we have a crash when using gurobi.Model.addConstrs with a
  quicksum on a list rather than on a generator.
  In other words, we need to get rid of [].
  See https://support.gurobi.com/hc/en-us/community/posts/28192464443281-model-addConstrs-breaking-sometimes-after-Python-version-change
  for more details.
- Add a test dedicated to LP_MRCPSP_GUROBI.

.github/workflows/build.yml

@@ -90,7 +90,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest", "macos-12", "macos-latest", "windows-latest"]
-        python-version: ["3.8", "3.11"]
+        python-version: ["3.8", "3.12"]
         wo_gurobi: ["", "without gurobi"]
         include:
           - os: "ubuntu-latest"
@@ -124,7 +124,7 @@ jobs:
           - coverage: false  # generally no coverage to avoid multiple reports
           - coverage: true  # coverage only for one entry of the matrix
             os: "ubuntu-latest"
-            python-version: "3.11"
+            python-version: "3.12"
             wo_gurobi: ""
         exclude:
           - os: "windows-latest"
@@ -139,7 +139,7 @@ jobs:
           - os: "macos-latest"
             python-version: "3.8"
           - os: "macos-12"
-            python-version: "3.11"
+            python-version: "3.12"
     runs-on: ${{ matrix.os }}
     defaults:
       run:
@@ -162,7 +162,7 @@ jobs:
         run: |
           python -m pip install -U pip
           wheelfile=$(ls ./dist/discrete_optimization*.whl)
-          pip install ${wheelfile}
+          pip install ${wheelfile} "cffi>=1.17"
       - name: Check import work without minizinc if DO_SKIP_MZN_CHECK set
         run: |
           export DO_SKIP_MZN_CHECK=1

discrete_optimization/rcpsp/solver/rcpsp_lp_solver.py

@@ -728,10 +728,8 @@ def init_model(self, **args):
             for j in variable_per_task
         )
         self.model.addConstrs(
-            gurobi.quicksum(
-                [key[2] * self.x[key] for key in variable_per_task[s]]
-                + [-key[2] * self.x[key] for key in variable_per_task[j]]
-            )
+            gurobi.quicksum(key[2] * self.x[key] for key in variable_per_task[s])
+            - gurobi.quicksum(key[2] * self.x[key] for key in variable_per_task[j])
             >= durations[j]
             for (j, s) in S
         )
@@ -781,11 +779,9 @@ def init_model(self, **args):
             if p_s.start_times is not None:
                 constraints = self.model.addConstrs(
                     gurobi.quicksum(
-                        [
-                            self.x[k]
-                            for k in self.variable_per_task[task]
-                            if k[2] == p_s.start_times[task]
-                        ]
+                        self.x[k]
+                        for k in self.variable_per_task[task]
+                        if k[2] == p_s.start_times[task]
                     )
                     == 1
                     for task in p_s.start_times

pyproject.toml

@@ -28,6 +28,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "Topic :: Software Development",
     "Topic :: Scientific/Engineering",
 ]

tests/pickup_vrp/solvers/test_ortools_solver.py

@@ -85,7 +85,7 @@ def test_ortools_with_cb(random_seed):
         parameters_cost=[ParametersCost(dimension_name="Distance", global_span=True)],
         local_search_metaheuristic=LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH,
         first_solution_strategy=first_solution_strategy,
-        time_limit=5,
+        time_limit=10,
         use_cp=use_cp,
         use_lns=use_lns,
         use_cp_sat=use_cp_sat,
@@ -105,7 +105,7 @@ def test_ortools_with_cb(random_seed):
     assert isinstance(fit, float)
     assert isinstance(sol, GPDPSolution)
     assert nb_iteration_tracker.nb_iteration > 0
-    assert (end_time - start_time).total_seconds() < 5
+    assert (end_time - start_time).total_seconds() < 10
 
 
 def test_ortools_with_warm_start(random_seed):
@@ -142,7 +142,7 @@ def test_ortools_with_warm_start(random_seed):
         parameters_cost=[ParametersCost(dimension_name="Distance", global_span=True)],
         local_search_metaheuristic=LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH,
         first_solution_strategy=first_solution_strategy,
-        time_limit=5,
+        time_limit=10,
         use_cp=use_cp,
         use_lns=use_lns,
         use_cp_sat=use_cp_sat,
@@ -173,7 +173,7 @@ def test_ortools_with_warm_start(random_seed):
         parameters_cost=[ParametersCost(dimension_name="Distance", global_span=True)],
         local_search_metaheuristic=LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH,
         first_solution_strategy=first_solution_strategy,
-        time_limit=5,
+        time_limit=10,
         use_cp=use_cp,
         use_lns=use_lns,
         use_cp_sat=use_cp_sat,

tests/rcpsp/solver/test_rcpsp_gurobi.py

@@ -0,0 +1,51 @@
+#  Copyright (c) 2022 AIRBUS and its affiliates.
+#  This source code is licensed under the MIT license found in the
+#  LICENSE file in the root directory of this source tree.
+
+import pytest
+
+from discrete_optimization.generic_tools.cp_tools import ParametersCP
+from discrete_optimization.rcpsp.rcpsp_parser import get_data_available, parse_file
+from discrete_optimization.rcpsp.solver.cpsat_solver import CPSatRCPSPSolver
+from discrete_optimization.rcpsp.solver.rcpsp_lp_solver import LP_MRCPSP_GUROBI
+
+try:
+    import gurobipy
+except ImportError:
+    gurobi_available = False
+else:
+    gurobi_available = True
+
+
+@pytest.mark.skipif(not gurobi_available, reason="You need Gurobi to test this solver.")
+@pytest.mark.parametrize(
+    "model",
+    ["j301_1.sm", "j1010_1.mm"],
+)
+def test_gurobi(model):
+    files_available = get_data_available()
+    file = [f for f in files_available if model in f][0]
+    rcpsp_problem = parse_file(file)
+    solver = LP_MRCPSP_GUROBI(problem=rcpsp_problem)
+
+    result_storage = solver.solve()
+
+    # test warm start
+    parameters_cp = ParametersCP.default()
+    parameters_cp.time_limit = 20
+    start_solution = (
+        CPSatRCPSPSolver(problem=rcpsp_problem)
+        .solve(parameters_cp=parameters_cp)
+        .get_best_solution_fit()[0]
+    )
+
+    # first solution is not start_solution
+    assert result_storage[0][0].rcpsp_schedule != start_solution.rcpsp_schedule
+
+    # warm start at first solution
+    solver = LP_MRCPSP_GUROBI(problem=rcpsp_problem)
+    solver.init_model()
+    solver.set_warm_start(start_solution)
+    # force first solution to be the hinted one
+    result_storage = solver.solve()
+    assert result_storage[0][0].rcpsp_schedule == start_solution.rcpsp_schedule