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dynamic_weighted_5_trucks.py
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dynamic_weighted_5_trucks.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from four_plus_truck_function import dyn_multi_opt
from show_routes import CreateMap
# Constants
B_TO_B = 100
B_TO_T = 10
N_WARDS = 3
N_TRUCKS = 5
W1 = 0.9
W2 = 0.1
# Set Random Seed
np.random.seed(42)
# Import Data
data = pd.read_csv('Data/Bin Locations.csv', index_col= 'id').sort_index()
distance = pd.read_csv('Data/distance.csv').drop('Unnamed: 0', axis = 1)
for i in range(distance.shape[0]):
distance.iloc[:, i] = distance.iloc[:, i]/np.max(distance.iloc[:, i])
# Optimization
# Ward 1 Optimization
data1 = data[data.Ward == 0]
visit1, visit2, visit3, visit4, visit5 = (
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
)
visitedNodes = set()
obj_value1 = dyn_multi_opt(data1, [visit1, visit2, visit3, visit4, visit5], visitedNodes = visitedNodes, distances = distance, ward_name = 'Truck 1', t_name = 'truck1', folder_Path = 'Data/Dynamic Data/Multiple Trucks/5 Trucks/', w1 = W1, w2 = W2, n_done = [0] * N_TRUCKS, n_trucks = N_TRUCKS)
print('\n Ward 1 Done \n')
# Ward 2 Optimization
data2 = data[data.Ward == 1]
visit1, visit2, visit3, visit4, visit5 = (
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
)
visitedNodes = set()
obj_value2 = dyn_multi_opt(data2, [visit1, visit2, visit3, visit4, visit5], visitedNodes = visitedNodes, distances = distance, ward_name = 'Truck 2', t_name = 'truck2', folder_Path = 'Data/Dynamic Data/Multiple Trucks/5 Trucks/', w1 = W1, w2 = W2, n_done = [0] * N_TRUCKS, n_trucks = N_TRUCKS)
print('\n Ward 2 Done \n')
# Ward 3 Optimization
data3 = data[data.Ward == 2]
visit1, visit2, visit3, visit4, visit5 = (
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
pd.DataFrame({'Node': pd.Series(0, dtype='int'), 'fill_ratio': pd.Series(0, dtype='float')}),
)
visitedNodes = set()
obj_value3 = dyn_multi_opt(data3, [visit1, visit2, visit3, visit4, visit5], visitedNodes = visitedNodes, distances = distance, ward_name = 'Truck 3', t_name = 'truck3', folder_Path = 'Data/Dynamic Data/Multiple Trucks/5 Trucks/', w1 = W1, w2 = W2, n_done = [0] * N_TRUCKS, n_trucks = N_TRUCKS)
print('\n Ward 3 Done \n')
# Collect Data
distance = pd.read_csv('Data/distance.csv').drop('Unnamed: 0', axis = 1)
path11 = []
path12 = []
path13 = []
path14 = []
path15 = []
path21 = []
path22 = []
path23 = []
path24 = []
path25 = []
path31 = []
path32 = []
path33 = []
path34 = []
path35 = []
v11 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 1/visited_truck1_1_{W1}_{W2}.csv')
v12 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 1/visited_truck1_2_{W1}_{W2}.csv')
v13 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 1/visited_truck1_3_{W1}_{W2}.csv')
v14 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 1/visited_truck1_4_{W1}_{W2}.csv')
v15 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 1/visited_truck1_5_{W1}_{W2}.csv')
v21 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 2/visited_truck2_1_{W1}_{W2}.csv')
v22 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 2/visited_truck2_2_{W1}_{W2}.csv')
v23 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 2/visited_truck2_3_{W1}_{W2}.csv')
v24 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 2/visited_truck2_4_{W1}_{W2}.csv')
v25 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 2/visited_truck2_5_{W1}_{W2}.csv')
v31 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 3/visited_truck3_1_{W1}_{W2}.csv')
v32 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 3/visited_truck3_2_{W1}_{W2}.csv')
v33 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 3/visited_truck3_3_{W1}_{W2}.csv')
v34 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 3/visited_truck3_4_{W1}_{W2}.csv')
v35 = pd.read_csv(f'Data/Dynamic Data/Multiple Trucks/5 Trucks/Visited Truck 3/visited_truck3_5_{W1}_{W2}.csv')
v11.Node = v11.Node.astype('int')
v12.Node = v12.Node.astype('int')
v13.Node = v13.Node.astype('int')
v14.Node = v14.Node.astype('int')
v15.Node = v15.Node.astype('int')
v21.Node = v21.Node.astype('int')
v22.Node = v22.Node.astype('int')
v23.Node = v23.Node.astype('int')
v24.Node = v24.Node.astype('int')
v25.Node = v25.Node.astype('int')
v31.Node = v31.Node.astype('int')
v32.Node = v32.Node.astype('int')
v33.Node = v33.Node.astype('int')
v34.Node = v34.Node.astype('int')
v35.Node = v35.Node.astype('int')
for i in range(len(v11) - 1):
path11.append((v11.iloc[i, 0], v11.iloc[i + 1, 0]))
for i in range(len(v12) - 1):
path12.append((v12.iloc[i, 0], v12.iloc[i + 1, 0]))
for i in range(len(v13) - 1):
path13.append((v13.iloc[i, 0], v13.iloc[i + 1, 0]))
for i in range(len(v14) - 1):
path14.append((v14.iloc[i, 0], v14.iloc[i + 1, 0]))
for i in range(len(v15) - 1):
path15.append((v15.iloc[i, 0], v15.iloc[i + 1, 0]))
for i in range(len(v21) - 1):
path21.append((v21.iloc[i, 0], v21.iloc[i + 1, 0]))
for i in range(len(v22) - 1):
path22.append((v22.iloc[i, 0], v22.iloc[i + 1, 0]))
for i in range(len(v23) - 1):
path23.append((v23.iloc[i, 0], v23.iloc[i + 1, 0]))
for i in range(len(v24) - 1):
path24.append((v24.iloc[i, 0], v24.iloc[i + 1, 0]))
for i in range(len(v25) - 1):
path25.append((v25.iloc[i, 0], v25.iloc[i + 1, 0]))
for i in range(len(v31) - 1):
path31.append((v31.iloc[i, 0], v31.iloc[i + 1, 0]))
for i in range(len(v32) - 1):
path32.append((v32.iloc[i, 0], v32.iloc[i + 1, 0]))
for i in range(len(v33) - 1):
path33.append((v33.iloc[i, 0], v33.iloc[i + 1, 0]))
for i in range(len(v34) - 1):
path34.append((v34.iloc[i, 0], v34.iloc[i + 1, 0]))
for i in range(len(v35) - 1):
path35.append((v35.iloc[i, 0], v35.iloc[i + 1, 0]))
gar11 = v11.iloc[-1,1]*10
gar12 = v12.iloc[-1,1]*10
gar13 = v13.iloc[-1,1]*10
gar14 = v14.iloc[-1,1]*10
gar15 = v15.iloc[-1,1]*10
gar21 = v21.iloc[-1,1]*10
gar22 = v22.iloc[-1,1]*10
gar23 = v23.iloc[-1,1]*10
gar24 = v24.iloc[-1,1]*10
gar25 = v25.iloc[-1,1]*10
gar31 = v31.iloc[-1,1]*10
gar32 = v32.iloc[-1,1]*10
gar33 = v33.iloc[-1,1]*10
gar34 = v34.iloc[-1,1]*10
gar35 = v35.iloc[-1,1]*10
dist11 = sum([distance.iloc[i,j] for i,j in path11])
dist12 = sum([distance.iloc[i,j] for i,j in path12])
dist13 = sum([distance.iloc[i,j] for i,j in path13])
dist14 = sum([distance.iloc[i,j] for i,j in path14])
dist15 = sum([distance.iloc[i,j] for i,j in path15])
dist21 = sum([distance.iloc[i,j] for i,j in path21])
dist22 = sum([distance.iloc[i,j] for i,j in path22])
dist23 = sum([distance.iloc[i,j] for i,j in path23])
dist24 = sum([distance.iloc[i,j] for i,j in path24])
dist25 = sum([distance.iloc[i,j] for i,j in path25])
dist31 = sum([distance.iloc[i,j] for i,j in path31])
dist32 = sum([distance.iloc[i,j] for i,j in path32])
dist33 = sum([distance.iloc[i,j] for i,j in path33])
dist34 = sum([distance.iloc[i,j] for i,j in path34])
dist35 = sum([distance.iloc[i,j] for i,j in path35])
v1 = [v11, v12, v13, v14, v15]
v2 = [v21, v22, v23, v24, v25]
v3 = [v31, v32, v33, v34, v35]
print('--------------- SAVING STATISTICS ----------------------\n')
# Save Statistics
stats = pd.DataFrame(
{
'Fill Ward 1 (in %)' : [
round(gar11, 4),
round(gar12, 4),
round(gar13, 4),
round(gar14, 4),
round(gar15, 4),
'-'],
'Garbage Fill Ward 1 (in Litres)' : [
round(gar11/10 * B_TO_B, 4),
round(gar12/10 * B_TO_B, 4),
round(gar13/10 * B_TO_B, 4),
round(gar14/10 * B_TO_B, 4),
round(gar15/10 * B_TO_B, 4),
'-'],
'Distance Travelled Ward 1 (in m)' : [
round(dist11, 4),
round(dist12, 4),
round(dist13, 4),
round(dist14, 4),
round(dist15, 4),
'-'],
'Garbage per Meter Ward 1 (in KG/m)' : [
round(gar11/dist11, 4),
round(gar12/dist12, 4),
round(gar13/dist13, 4),
round(gar14/dist14, 4),
round(gar15/dist15, 4),
'-'],
'Percentage of Bins covered Ward 1 (in %)' : [
round( 100 * (v11.shape[0] - 2)/ data[data.Ward == 0].shape[0], 4),
round( 100 * (v12.shape[0] - 2)/ data[data.Ward == 0].shape[0], 4),
round( 100 * (v13.shape[0] - 2)/ data[data.Ward == 0].shape[0], 4),
round( 100 * (v14.shape[0] - 2)/ data[data.Ward == 0].shape[0], 4),
round( 100 * (v15.shape[0] - 2)/ data[data.Ward == 0].shape[0], 4),
round( 100 * np.sum([i.shape[0] - 2 for i in v1])/ data[data.Ward == 0].shape[0], 4)],
'Fill Ward 2 (in %)' : [
round(gar21, 4),
round(gar22, 4),
round(gar23, 4),
round(gar24, 4),
round(gar25, 4),
'-'],
'Garbage Fill Ward 2 (in Litres)' : [
round(gar21/10 * B_TO_B, 4),
round(gar22/10 * B_TO_B, 4),
round(gar23/10 * B_TO_B, 4),
round(gar24/10 * B_TO_B, 4),
round(gar25/10 * B_TO_B, 4),
'-'],
'Distance Travelled Ward 2 (in m)' : [
round(dist21, 4),
round(dist22, 4),
round(dist23, 4),
round(dist24, 4),
round(dist25, 4),
'-'],
'Garbage per Meter Ward 2 (in KG/m)' : [
round(gar21/dist21, 4),
round(gar22/dist22, 4),
round(gar23/dist23, 4),
round(gar24/dist24, 4),
round(gar25/dist25, 4),
'-'],
'Percentage of Bins covered Ward 2 (in %)' : [
round( 100 * (v21.shape[0] - 2)/ data[data.Ward == 1].shape[0], 4),
round( 100 * (v22.shape[0] - 2)/ data[data.Ward == 1].shape[0], 4),
round( 100 * (v23.shape[0] - 2)/ data[data.Ward == 1].shape[0], 4),
round( 100 * (v24.shape[0] - 2)/ data[data.Ward == 1].shape[0], 4),
round( 100 * (v25.shape[0] - 2)/ data[data.Ward == 1].shape[0], 4),
round( 100 * np.sum([i.shape[0] - 2 for i in v2])/ data[data.Ward == 1].shape[0], 4)],
'Fill Ward 3 (in %)' : [
round(gar31, 4),
round(gar32, 4),
round(gar33, 4),
round(gar34, 4),
round(gar35, 4),
'-'],
'Garbage Fill Ward 3 (in Litres)' : [
round(gar31/10 * B_TO_B, 4),
round(gar32/10 * B_TO_B, 4),
round(gar33/10 * B_TO_B, 4),
round(gar34/10 * B_TO_B, 4),
round(gar35/10 * B_TO_B, 4),
'-'],
'Distance Travelled Ward 3 (in m)' : [
round(dist31, 4),
round(dist32, 4),
round(dist33, 4),
round(dist34, 4),
round(dist35, 4),
'-'],
'Garbage per Meter Ward 3 (in KG/m)' : [
round(gar31/dist31, 4),
round(gar32/dist32, 4),
round(gar33/dist33, 4),
round(gar34/dist34, 4),
round(gar35/dist35, 4),
'-'],
'Percentage of Bins covered Ward 3 (in %)' : [
round( 100 * (v31.shape[0] - 2)/ data[data.Ward == 2].shape[0], 4),
round( 100 * (v32.shape[0] - 2)/ data[data.Ward == 2].shape[0], 4),
round( 100 * (v33.shape[0] - 2)/ data[data.Ward == 2].shape[0], 4),
round( 100 * (v34.shape[0] - 2)/ data[data.Ward == 2].shape[0], 4),
round( 100 * (v35.shape[0] - 2)/ data[data.Ward == 2].shape[0], 4),
round( 100 * np.sum([i.shape[0] - 2 for i in v3])/ data[data.Ward == 2].shape[0], 4)],
}, index=['Truck 1', 'Truck 2', 'Truck 3', 'Truck 4', 'Truck 5', 'Total Percentage'])
stats.to_csv('Data/Dynamic Data/Multiple Trucks/5 Trucks/Statistics.csv')
print('--------------- GENERATING MAP ----------------------')
# Plotting routes
map = CreateMap()
map.createRoutes('Data/Dynamic Data/Multiple Trucks/5 Trucks/', N_WARDS, N_TRUCKS, W1, W2, Multiple_truck = True)
map.createLatLong('Data/Bin Locations.csv', N_WARDS)
map.createRoutesDict(N_WARDS)
map.addRoutesToMap(N_WARDS, N_TRUCKS)
map.addDepot()
map.addNodes('Data/Bin Locations.csv')
map.saveMap('Data/Dynamic Data/Multiple Trucks/5 Trucks/')
map.displayMap('Data/Dynamic Data/Multiple Trucks/5 Trucks/')