import CostFunction as pro
import Drone as pre
import Visualize as post
import matplotlib.pyplot as plt
import numpy as np
drone1 = pre.Drone(70, 7.7, 0.009)
drone2 = pre.Drone(200, 10.5, 0.012)
drone3 = pre.Drone(450, 13.2, 0.018)
wind1 = pre.Wind(np.random.uniform(2.0, 3.5), 0)
# this time, we consider that wind goes from west to east, and its magnitude goes from 2.0 m/s to 3.5 m/s
depot1 = pre.Depot("Chatenay-Malabry", 0, 0)
param1 = pre.DeliveryParameters(drone1, wind1, pro.cost_b)
total_cost = 0
total_savings = 0
for i in range(500):
print(i)
problem_i = pre.Problem(depot1)
problem_i.generate_random_clients(amount=np.random.randint(50, 60),
x=(-6000, 6000),
y=(-3500, 3500),
demand=(5, 60))
pro.clarke_and_wright(problem_i, param1, version="sequential")
solution_i = problem_i.solutions_list[0]
total_cost += solution_i.cost_and_savings()[0]
total_savings += solution_i.cost_and_savings()[1]
average = total_savings / (total_savings + total_cost)
"""Case study - Partitioning clients' lists for pb250_b"""
import Drone as dro
import CostFunction as cost
import Visualize as viz
import matplotlib.pyplot as plt
import numpy as np
drone1 = dro.Drone(180, 10.3, 0.013)
drone2 = dro.Drone(510, 12.5, 0.024)
wind1 = dro.Wind(0, 0)
param1 = dro.DeliveryParameters(drone1, wind1, cost.cost_b)
param2 = dro.DeliveryParameters(drone2, wind1, cost.cost_b)
problem_g = dro.Problem()
problem_g.import_csv('pb250_b.csv')
best_cost = float('inf')
limit = 0
for i in range(5, 151):
clients_list_1 = []
clients_list_2 = []
for client in problem_g.clients_list:
if client.demand > i:
clients_list_2.append(client)
else:
clients_list_1.append(client)
problem1 = cost.Problem(problem_g.depot, clients_list_1)
problem2 = cost.Problem(problem_g.depot, clients_list_2)
