improved tour length

backend/src/parameters/optimizer_parameters.yaml

@@ -3,3 +3,4 @@ detour_corridor_width: 300
 average_walking_speed: 4.8
 max_landmarks: 10
 max_landmarks_refiner: 20
+overshoot: 1.4

backend/src/tester.py

@@ -63,6 +63,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
     logger.info("Optimized route : ")
     for l in linked_tour :
         logger.info(f"{l}")
+    logger.info(f"Estimated length of tour : {linked_tour.total_time}")
 
     # with open('linked_tour.yaml', 'w') as f:
     #     yaml.dump(linked_tour.asdict(), f)

backend/src/utils/optimizer.py

@@ -17,10 +17,11 @@ class Optimizer:
 
     logger = logging.getLogger(__name__)
 
-    detour: int = None          # accepted max detour time (in minutes)
-    detour_factor: float        # detour factor of straight line vs real distance in cities
-    average_walking_speed: float        # average walking speed of adult
-    max_landmarks: int          # max number of landmarks to visit
+    detour: int = None              # accepted max detour time (in minutes)
+    detour_factor: float            # detour factor of straight line vs real distance in cities
+    average_walking_speed: float    # average walking speed of adult
+    max_landmarks: int              # max number of landmarks to visit
+    overshoot: float                # overshoot to allow maxtime to overflow. Optimizer is a bit restrictive
 
 
     def __init__(self) :
@@ -31,6 +32,7 @@ class Optimizer:
             self.detour_factor = parameters['detour_factor']
             self.average_walking_speed = parameters['average_walking_speed']
             self.max_landmarks = parameters['max_landmarks']
+            self.overshoot = parameters['overshoot']
         
 
 
@@ -167,7 +169,7 @@ class Optimizer:
 
 
 
-    def init_ub_dist(self, landmarks: list[Landmark], max_steps: int):
+    def init_ub_dist(self, landmarks: list[Landmark], max_time: int):
         """
         Initialize the objective function coefficients and inequality constraints for the optimization problem.
 
@@ -176,7 +178,7 @@ class Optimizer:
 
         Args:
             landmarks (list[Landmark]): List of landmarks.
-            max_steps (int): Maximum number of steps allowed.
+            max_time (int): Maximum time of visit allowed.
 
         Returns:
             Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
@@ -200,7 +202,7 @@ class Optimizer:
             A_ub += dist_table
         c = c*len(landmarks)
 
-        return c, A_ub, [max_steps]
+        return c, A_ub, [max_time*self.overshoot]
 
 
     def respect_number(self, L, max_landmarks: int):
@@ -474,7 +476,7 @@ class Optimizer:
         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_steps is limiting factor)
+        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