diff --git a/discrete_optimization/facility/solvers/toulbar.py b/discrete_optimization/facility/solvers/toulbar.py
index 837e1693..826eccad 100644
--- a/discrete_optimization/facility/solvers/toulbar.py
+++ b/discrete_optimization/facility/solvers/toulbar.py
@@ -67,7 +67,10 @@ def init_model_boolean_variable(self, **kwargs):
         """
         nb_facilities = self.problem.facility_count
         nb_customers = self.problem.customer_count
-        model = pytoulbar2.CFN(kwargs.get("upper_bound", 10e8))
+        if "vns" in kwargs:
+            model = pytoulbar2.CFN(kwargs.get("upper_bound", 10e8), vns=kwargs["vns"])
+        else:
+            model = pytoulbar2.CFN(kwargs.get("upper_bound", 10e8))
         x: Dict[Tuple[int, int], Union[int, Any]] = {}
         key_to_index = {}
         index = 0
@@ -133,7 +136,10 @@ def init_model_integer_variable(self, **kwargs):
         """
         nb_facilities = self.problem.facility_count
         nb_customers = self.problem.customer_count
-        model = pytoulbar2.CFN(kwargs.get("upper_bound", 10e8))
+        if "vns" in kwargs:
+            model = pytoulbar2.CFN(kwargs.get("upper_bound", 10e8), vns=kwargs["vns"])
+        else:
+            model = pytoulbar2.CFN(kwargs.get("upper_bound", 10e8))
         index = 0
         matrix_fc_indicator, matrix_length = prune_search_space(
             self.problem, n_cheapest=nb_facilities, n_shortest=nb_facilities
diff --git a/discrete_optimization/generic_tools/toulbar_tools.py b/discrete_optimization/generic_tools/toulbar_tools.py
index 161bcf97..7fea5530 100644
--- a/discrete_optimization/generic_tools/toulbar_tools.py
+++ b/discrete_optimization/generic_tools/toulbar_tools.py
@@ -19,6 +19,7 @@
 
 import logging
 
+from discrete_optimization.generic_tools.callbacks.callback import CallbackList
 from discrete_optimization.generic_tools.do_solver import SolverDO
 from discrete_optimization.generic_tools.hyperparameters.hyperparameter import (
     CategoricalHyperparameter,
@@ -53,12 +54,16 @@ def solve(
     ) -> ResultStorage:
         if self.model is None:
             self.init_model(**kwargs)
+        callback = CallbackList(callbacks)
+        callback.on_solve_start(solver=self)
         solution = self.model.Solve(showSolutions=1, timeLimit=int(time_limit))
         logger.info(
             f"Solution value = {solution[1]}, bound={self.model.GetDDualBound()}"
         )
         sol = self.retrieve_solution(solution)
-        return self.create_result_storage([(sol, self.aggreg_from_sol(sol))])
+        res = self.create_result_storage([(sol, self.aggreg_from_sol(sol))])
+        callback.on_solve_end(res=res, solver=self)
+        return res
 
 
 def to_lns_toulbar(cls: Type[ToulbarSolver]):
diff --git a/discrete_optimization/rcpsp/solvers/toulbar.py b/discrete_optimization/rcpsp/solvers/toulbar.py
index 3aa16c49..14dc55af 100644
--- a/discrete_optimization/rcpsp/solvers/toulbar.py
+++ b/discrete_optimization/rcpsp/solvers/toulbar.py
@@ -63,9 +63,11 @@ def init_model(self, **kwargs: Any) -> None:
             )
             raise exc
         if "vns" in kwargs:
-            model = pytoulbar2.CFN(ubinit=self.problem.horizon, vns=kwargs["vns"])
+            model = pytoulbar2.CFN(
+                ubinit=kwargs.get("ub", self.problem.horizon), vns=kwargs["vns"]
+            )
         else:
-            model = pytoulbar2.CFN(ubinit=self.problem.horizon)
+            model = pytoulbar2.CFN(ubinit=kwargs.get("ub", self.problem.horizon))
         n_jobs = self.problem.n_jobs
         horizon = self.problem.horizon
         index_var = 0