def main():
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
nodes = tsu.generate_nodes()
cost = tsu.calculate_distances(nodes)
salesmen = np.random.randint(2, 4)
salesmen = 2
nodes = []
nodes = np.array([[0, 4], [-1.5, 0], [1.5, 0], [-1.5, 0], [-1.5, 1],
[-0.5, 2], [-0.5, 3], [0.5, 3], [0.5, 2], [1.5, 0],
[1.5, 1]])
#nodes = np.array([[0, 3], [1, 1], [-1, 1], [1, 2], [1, 3], [-1, 3], [-1, 2]])
cost = tsu.calculate_distances(nodes)
solution, objective, _ = tsu.solve_problem(
tlpp_solver,
cost,
salesmen=salesmen,
areas=[0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
# solution, objective, _ = tsu.solve_problem(tlpp_solver, cost, salesmen=salesmen, areas=[0, 0, 0, 1, 0, 1, 0])
fig, ax = tsu.plot_problem(nodes, solution, objective)
plt.show()
def main():
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
nodes = tsu.generate_nodes()
cost = tsu.calculate_distances(nodes)
solution, objective, _ = tsu.solve_problem(tsp_solver, cost)
fig, ax = tsu.plot_problem(nodes, solution, objective)
plt.show()
def main():
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
nodes = tsu.generate_nodes()
cost = tsu.calculate_distances(nodes)
solution, cost_total, _ = tsu.solve_problem(ovrp_solver, cost)
fig, ax = tsu.plot_problem(nodes, solution, cost_total)
plt.show()
def main():
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
nodes = tsu.generate_nodes()
cost = tsu.calculate_distances(nodes)
salesmen = np.random.randint(2, 4)
solution, objective, _ = tsu.solve_problem(mtsp_solver, cost, salesmen=salesmen)
fig, ax = tsu.plot_problem(nodes, solution, objective)
plt.show()
def main():
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
nodes = tsu.generate_nodes()
cost = tsu.calculate_distances(nodes)
solution, cost_total, _ = tsu.solve_problem(movrp_solver,
cost,
num_agents=3)
fig, ax = tsu.plot_problem(nodes, solution, cost_total)
plt.show()
def main():
import matplotlib as mpl
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
import random
mpl.rcParams['figure.facecolor'] = 'white'
nodes = tsu.generate_nodes(n=40)
cost = tsu.calculate_distances(nodes)
nodes = []
random.seed(42)
nodes.append([0, 0])
for i in range(-1, 3):
for j in range(2, 0, -1):
ni = i
nj = j
# ni = random.uniform(-0.5,0.5) + i
# nj = random.uniform(-0.5,0.5) + j
nodes.append([ni, nj])
nodes.append([1, 0])
nodes = np.array(nodes)
print(nodes)
cost = tsu.calculate_distances(nodes)
print(cost)
from math import sqrt
max_cost = [sqrt(5) * 2.5 + sqrt(2)]
for mc in max_cost:
solution, objective, _ = tsu.solve_problem(inspection_op_solver,
cost,
cost_max=mc,
output_flag=1,
time_limit=36000,
mip_gap=0.001)
print("Objective: {0}".format(objective))
# fig, ax = tsu.plot_problem(nodes, solution, objective)
# solution = [0, 3, 4, 9, 10, 15, 20, 25, 19, 13, 12, 7, 1, 6, 11, 16, 21, 22, 23, 26]
# fig, ax = tsu.plot_problem_correlation_gradient(nodes, solution, objective)
# ax.axis('equal')
# plt.show()
# plt.savefig('miqp-grad.png', dpi=300)
fig, ax = tsu.plot_problem(nodes, solution, objective)
ax.axis('equal')
plt.show()
def main():
import matplotlib as mpl
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
import random
mpl.rcParams['figure.facecolor'] = 'white'
nodes = tsu.generate_nodes(n=40)
cost = tsu.calculate_distances(nodes)
nodes = []
random.seed(42)
nodes.append([0, 0])
for i in range(1, 6):
for j in range(-2, 3):
ni = i
nj = j
# ni = random.uniform(-0.5,0.5) + i
# nj = random.uniform(-0.5,0.5) + j
nodes.append([ni, nj])
nodes.append([6, 0])
nodes = np.array(nodes)
cost = tsu.calculate_distances(nodes)
max_cost = [25.5]
mc = [13.5, 13.5]
num_robots = 2
# for mc in max_cost:
solution, objective, _ = tsu.solve_problem(ctop_solver,
cost,
num_robots=num_robots,
cost_max=mc,
output_flag=1,
time_limit=36000,
mip_gap=0.0)
# util = 0
# print(solution)
# for i in solution:
# extras = 0
# if i != 0 and i != solution[len(solution)-1]:
# for j in range(cost.shape[0]):
# if j != i and j not in solution and j != 0 and j != solution[len(solution)-1] and cost[i,j] < 2:
# extras += np.e**(-2*cost[i,j])
# util += 1 + extras
#
# print("Utility: {0}".format(util))
fig, ax = tsu.plot_problem(nodes, solution, objective)
plt.show()
def main():
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
nodes = tsu.generate_nodes()
cost = tsu.calculate_distances(nodes)
salesmen = np.random.randint(2, 4)
constraints = np.random.randint(10, 100, salesmen).tolist()
solution, objective, _ = tsu.solve_problem(ccmmtsp_solver,
cost,
salesmen=salesmen,
constraints=constraints)
fig, ax = tsu.plot_problem(nodes, solution, objective)
plt.show()
def main():
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
import random
nodes = tsu.generate_nodes(n=100, lb=-100, up=100, dims=2)
cost = tsu.calculate_distances(nodes)
nodes = []
random.seed(42)
nodes.append([0,0])
for i in range(1,6):
for j in range(-2,3):
ni = i
nj = j
# ni = random.uniform(-0.5,0.5) + i
# nj = random.uniform(-0.5,0.5) + j
nodes.append([ni,nj])
nodes.append([6,0])
nodes = np.array(nodes)
cost = tsu.calculate_distances(nodes)
max_cost = [25.5]
for mc in max_cost:
solution, objective, _ = tsu.solve_problem(op_solver, cost, cost_max=mc, output_flag=1, mip_gap=0.0, time_limit=3600)
util = 0
for i in solution:
extras = 0
if i != 0 and i != solution[len(solution)-1]:
for j in range(cost.shape[0]):
if j != i and j not in solution and j != 0 and j != solution[len(solution)-1]:
extras += np.e**(-2*cost[i,j])
util += 1 + extras
print("Utility: {0}".format(util))
fig, ax = tsu.plot_problem(nodes, solution, objective)
plt.show()
def main():
import matplotlib as mpl
import matplotlib.pyplot as plt
import task_scheduling.utils as tsu
import random
mpl.rcParams['figure.facecolor'] = 'white'
nodes = tsu.generate_nodes(n=40)
cost = tsu.calculate_distances(nodes)
nodes = []
random.seed(42)
nodes.append([0,0])
for i in range(1,6):
for j in range(-2,3):
ni = i
nj = j
# ni = random.uniform(-0.5,0.5) + i
# nj = random.uniform(-0.5,0.5) + j
nodes.append([ni,nj])
nodes.append([6,0])
nodes = np.array(nodes)
cost = tsu.calculate_distances(nodes)
max_cost = [25.5]
for mc in max_cost:
solution, objective, _ = tsu.solve_problem(cop_solver, cost, cost_max=mc ,output_flag=1, time_limit=36000, mip_gap=0.1)
utility = calculate_utility(solution, cost)
print("Utility: {0}".format(utility))
fig, ax = tsu.plot_problem(nodes, solution, objective)
ax.axis('equal')
plt.show()