diff --git a/backend/src/tests/test_main.py b/backend/src/tests/test_main.py
index 60e1278..9feceb3 100644
--- a/backend/src/tests/test_main.py
+++ b/backend/src/tests/test_main.py
@@ -54,7 +54,7 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     assert len(landmarks) > 2           # check that there is something to visit
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
     # assert 2==3
-
+'''
 def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     """
     Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
@@ -214,7 +214,7 @@ def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
     assert response.status_code == 200  # check for successful planning
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
-
+'''
 
 # def test_new_trip_single_prefs(client):
 #     response = client.post(
diff --git a/backend/src/utils/optimizer.py b/backend/src/utils/optimizer.py
index d7f354e..e951737 100644
--- a/backend/src/utils/optimizer.py
+++ b/backend/src/utils/optimizer.py
@@ -466,18 +466,27 @@ class Optimizer:
 
         # SET CONSTRAINTS FOR EQUALITY
         A_eq, b_eq = self.init_eq_not_stay(L)                            # Force solution not to stay in same place
-        A, b = self.respect_user_must_do(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
-        A_eq = np.vstack((A_eq, A), dtype=np.int8)
-        b_eq += b
-        A, b = self.respect_user_must_avoid(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
-        A_eq = np.vstack((A_eq, A), dtype=np.int8)
-        b_eq += b
         A, b = self.respect_start_finish(L)                  # Force start and finish positions
         A_eq = np.vstack((A_eq, A), dtype=np.int8)
         b_eq += b
         A, b = self.respect_order(L)                         # Respect order of visit (only works when max_time is limiting factor)
         A_eq = np.vstack((A_eq, A), dtype=np.int8)
         b_eq += b
+        A, b = self.respect_user_must_do(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
+        A_eq = np.vstack((A_eq, A), dtype=np.int8)
+        b_eq += b
+        A, b = self.respect_user_must_avoid(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
+        A_eq = np.vstack((A_eq, A), dtype=np.int8)
+        b_eq += b
+        
+
+        print(A_ub)
+        print('\n\n')
+        print(b_ub)
+        print('\n\n')
+        print(A_eq)
+        print('\n\n')
+        print(b_eq)
         
         # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
         x_bounds = [(0, 1)]*L*L