#!/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
# argv[1] Folder of VRPTW Solomon/Homberger benchmarks from vroom-scripts
# argv[2] Json file with minimal settings:
# "Vehicle Definitions"
# "Available Fleets"
# "Problem-Vehicle Combinations Mapping"
# argv[3] Output file name
# argv[4] Scaling factor, Multiplies the values in matrices
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
if "jobs" and "shipments" in json_input.keys():
inner_dict = {
problem_definition_name: {
"Customers": {
"jobs": json_input["jobs"],
"shipments": json_input["shipments"],
},
"Matrices": json_input["matrices"],
}
}
elif "jobs" in json_input.keys():
inner_dict = {
problem_definition_name: {
"Customers": {"jobs": json_input["jobs"]},
"Matrices": json_input["matrices"],
}
}
elif "shipments" in json_input.keys():
inner_dict = {
problem_definition_name: {
"Customers": {"shipments": json_input["shipments"]},
"Matrices": json_input["matrices"],
}
}
json_data["Problem Definitions"].update(inner_dict)
# json_input = parse_vrptw(input_file)
# index=0
problem_definitions = json_data["Problem Definitions"]
for k, v in problem_definitions.items():
# scaling time_windows at customers
if "jobs" in v["Customers"]:
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["Problem Definitions"][k]["Customers"]["jobs"][i][
"time_windows"
][0][0]
* scaling_factor
)
json_data["Problem Definitions"][k]["Customers"]["jobs"][i][
"time_windows"
][0][1] = (
json_data["Problem Definitions"][k]["Customers"]["jobs"][i][
"time_windows"
][0][1]
* scaling_factor
)
if "shipments" in v["Customers"]:
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["Problem Definitions"][k]["Customers"]["shipments"][i][
"time_windows"
][0][0]
* scaling_factor
)
json_data["Problem Definitions"][k]["Customers"]["shipments"][i][
"time_windows"
][0][1] = (
json_data["Problem Definitions"][k]["Customers"]["shipments"][i][
"time_windows"
][0][1]
* scaling_factor
)
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)