Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
import sys
import math
import os
# Generate a json-formatted problem from a TSPTW/VRPTW file.
# Those benchmarks use double precision for matrix costs (and input
# timings), and results are usually reported with 2 decimal places. As
# a workaround, we multiply all costs/timings by CUSTOM_PRECISION
# before performing the usual integer rounding. Comparisons in
# benchmarks/compare_to_BKS.py are adjusted accordingly.
CUSTOM_PRECISION = 10
def nint(x):
return int(x + 0.5)
def euc_2D(c1, c2, PRECISION=1):
xd = c1[0] - c2[0]
yd = c1[1] - c2[1]
return nint(PRECISION * math.sqrt(xd * xd + yd * yd))
line_no = 0
def get_matrix(coords, PRECISION=1):
N = len(coords)
matrix = [[0 for i in range(N)] for j in range(N)]
for i in range(N):
for j in range(i + 1, N):
value = euc_2D(coords[i], coords[j], PRECISION)
matrix[i][j] = value
matrix[j][i] = value
return matrix
def parse_meta(lines, meta):
global line_no
while len(lines) > 0:
line = lines.pop(0).strip()
line_no += 1
if len(line) == 0:
continue
elif "CUSTOMER" in line or "CUST NO." in line:
lines.insert(0, line)
line_no -= 1
break
elif "NUMBER" in line:
continue
else:
x = line.split()
if len(x) < 2:
print("Cannot understand line " + str(line_no) + ": too few columns.")
exit(2)
meta["VEHICLES"] = int(x[0])
meta["CAPACITY"] = int(x[1])
def parse_jobs(lines, jobs, coords):
global line_no
location_index = 0
while len(lines) > 0:
line = lines.pop(0).strip()
line_no += 1
if len(line) == 0:
continue
elif "CUST " in line:
continue
else:
x = line.split()
if len(x) < 7:
print("Cannot understand line " + str(line_no) + ": too few columns.")
exit(2)
# some guys use '999' entry as terminator sign and others don't
elif "999" in x[0] and len(jobs) < 999:
break
coords.append([float(x[1]), float(x[2])])
jobs.append(
{
"id": int(x[0]),
"location": [float(x[1]), float(x[2])],
"location_index": location_index,
"delivery": [int(float(x[3]))],
"time_windows": [
[
CUSTOM_PRECISION * int(float(x[4])),
CUSTOM_PRECISION * int(float(x[5])),
]
],
"service": CUSTOM_PRECISION * int(x[6]),
}
)
location_index += 1
def parse_vrptw(input_file):
global line_no
with open(input_file, "r") as f:
lines = f.readlines()
meta = {}
while len(lines) > 0:
line = lines.pop(0).strip()
line_no += 1
if len(line) > 0:
if "#NUM" in line:
lines.insert(0, line)
meta["NAME"] = input_file
else:
meta["NAME"] = line
break
coords = []
jobs = []
while len(lines) > 0:
line = lines.pop(0)
line_no += 1
if "VEHICLE" in line:
parse_meta(lines, meta)
elif "CUSTOMER" in line or "CUST " in line or "#NUM" in line:
parse_jobs(lines, jobs, coords)
matrix = get_matrix(coords, CUSTOM_PRECISION)
j = jobs.pop(0)
total_demand = 0
time_min = ~0
time_max = 0
for n in range(len(jobs)):
total_demand += jobs[n]["delivery"][0]
for t in jobs[n]["time_windows"]:
if t[0] - matrix[0][n] < time_min:
time_min = t[0] - matrix[0][n]
if t[1] + matrix[n][0] > time_max:
time_max = t[1] + matrix[n][0]
return {
"jobs": jobs,
"matrices": {"car": {"durations": matrix}},
}
if __name__ == "__main__":
input_file_folder_path = sys.argv[1]
# input_file_folder_path = "/home/david/vroom-scripts/benchmarks/VRPTW/solomon"
vehicle_fleet_settings = sys.argv[2]
# vehicle_fleet_settings = os.getcwd() + "/json/minimal_json_definitions.json"
output_file_name = sys.argv[3]
# output_file_name = "generated_preprocessed_from_scripts.json"
# output_name = input_file[: input_file.rfind(".txt")] + ".json"
scaling_factor = sys.argv[4]
scaling_factor = int(scaling_factor)
if scaling_factor <= 0:
raise TypeError("4th argument is not positive integer.")
# print("- Writing problem " + input_file + " to " + output_name)
name_of_folder = input_file_folder_path.split("/")
name_of_folder = name_of_folder[-1]
json_file = open(vehicle_fleet_settings, "r")
# json_file = open("json/Ostrava.json", "r")
data = json_file.read()
json_file.close()
json_data = json.loads(data)
files = os.listdir(input_file_folder_path)
json_data.update({"Problem Definitions": {}})
problems = []
for file in files:
json_input = parse_vrptw(input_file_folder_path + "/" + file)
name_of_problem = file.replace(".txt", "")
problem_definition_name = name_of_folder + "_" + name_of_problem
inner_dict = {problem_definition_name: json_input}
json_data["Problem Definitions"].update({problem_definition_name: json_input})
# json_input = parse_vrptw(input_file)
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
# index=0
problem_definitions = json_data["Problem Definitions"]
# for k, v in problem_definitions.items():
# for i in range(len(v["Customers"]["jobs"])):
# json_data["Problem Definitions"][k]['Customers']['jobs'][i]['service']=round(problem_definitions[k]['Customers']['jobs'][i]['service']*scaling_factor)
# json_data["Problem Definitions"][k]['Customers']['jobs'][i]['time_windows'][0][0]=json_data["Vehicle Definitions"][2]["Big ICE"]["time_window"][0]
# json_data["Problem Definitions"][k]['Customers']['jobs'][i]['time_windows'][0][1]=json_data["Vehicle Definitions"][2]["Big ICE"]["time_window"][1]
# for i in range(len(v["Customers"]["shipments"])):
# json_data["Problem Definitions"][k]['Customers']['shipments'][i]['service']=round(problem_definitions[k]['Customers']['shipments'][i]['service']*scaling_factor)
# json_data["Problem Definitions"][k]['Customers']['shipments'][i]['time_windows'][0][0]=json_data["Vehicle Definitions"][2]["Big ICE"]["time_window"][0]
# json_data["Problem Definitions"][k]['Customers']['shipments'][i]['time_windows'][0][1]=json_data["Vehicle Definitions"][2]["Big ICE"]["time_window"][1]
for k, v in problem_definitions.items():
for i in range(len(v["matrices"])):
single_matrix = v["matrices"]["car"]["durations"]
for j in range(len(single_matrix)):
single_row = single_matrix[j]
for l in range(len(single_row)):
json_data["Problem Definitions"][k]["matrices"]["car"]["durations"][
j
][l] = round(
json_data["Problem Definitions"][k]["matrices"]["car"][
"durations"
][j][l]
* scaling_factor
)
if ".json" not in output_file_name:
output_file_name.replace(".", "")
output_file_name = output_file_name + ".json"
with open(output_file_name, "w") as out:
json.dump(json_data, out)