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improved tour length
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Helldragon67 2024-09-10 17:23:01 +02:00
parent 797db5c1da
commit d1c53e08bb
3 changed files with 12 additions and 8 deletions
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1
backend/src/parameters/optimizer_parameters.yaml
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@ -3,3 +3,4 @@ detour_corridor_width: 300
average_walking_speed: 4.8 average_walking_speed: 4.8
max_landmarks: 10 max_landmarks: 10
max_landmarks_refiner: 20 max_landmarks_refiner: 20
overshoot: 1.4

1
backend/src/tester.py
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@ -63,6 +63,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
logger.info("Optimized route : ") logger.info("Optimized route : ")
for l in linked_tour : for l in linked_tour :
logger.info(f"{l}") logger.info(f"{l}")
logger.info(f"Estimated length of tour : {linked_tour.total_time}")
# with open('linked_tour.yaml', 'w') as f: # with open('linked_tour.yaml', 'w') as f:
# yaml.dump(linked_tour.asdict(), f) # yaml.dump(linked_tour.asdict(), f)

10
backend/src/utils/optimizer.py
View File

@ -21,6 +21,7 @@ class Optimizer:
detour_factor: float # detour factor of straight line vs real distance in cities detour_factor: float # detour factor of straight line vs real distance in cities
average_walking_speed: float # average walking speed of adult average_walking_speed: float # average walking speed of adult
max_landmarks: int # max number of landmarks to visit 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) : def __init__(self) :
@ -31,6 +32,7 @@ class Optimizer:
self.detour_factor = parameters['detour_factor'] self.detour_factor = parameters['detour_factor']
self.average_walking_speed = parameters['average_walking_speed'] self.average_walking_speed = parameters['average_walking_speed']
self.max_landmarks = parameters['max_landmarks'] 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. Initialize the objective function coefficients and inequality constraints for the optimization problem.
@ -176,7 +178,7 @@ class Optimizer:
Args: Args:
landmarks (list[Landmark]): List of landmarks. landmarks (list[Landmark]): List of landmarks.
max_steps (int): Maximum number of steps allowed. max_time (int): Maximum time of visit allowed.
Returns: Returns:
Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint. 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 A_ub += dist_table
c = c*len(landmarks) 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): 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, b = self.respect_start_finish(L) # Force start and finish positions
A_eq = np.vstack((A_eq, A), dtype=np.int8) A_eq = np.vstack((A_eq, A), dtype=np.int8)
b_eq += b 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) A_eq = np.vstack((A_eq, A), dtype=np.int8)
b_eq += b b_eq += b