-
Notifications
You must be signed in to change notification settings - Fork 1
/
main.py
319 lines (270 loc) · 8.26 KB
/
main.py
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
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
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
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
import pandas as pd
from openpyxl import load_workbook
import os
from math import radians, cos, sin, acos, asin
from routeflows import RouteFlows
from facilitylocation import FacilityLocationModel
class CapEx:
def __init__(self, filename='InputData - Copy.xlsx'):
self.readData(filename)
self.processData()
def readData(self, filename):
'''Method reads following input tables from excel file
named 'InputData.xlsx' with corresponding sheet names
'''
try:
self.df = {}
workbook = pd.ExcelFile(filename)
self.df['customers'] = pd.read_excel(
workbook, 'Customers')
self.df['sites'] = pd.read_excel(
workbook, 'Sites')
self.df['periods'] = pd.read_excel(
workbook, 'Periods')
self.df['demand'] = pd.read_excel(
workbook, 'CustomerDemand')
self.df['sitecapacity'] = pd.read_excel(
workbook, 'SiteCapacity')
self.df['scenarios'] = pd.read_excel(
workbook, 'Scenarios')
except IOError:
print("Error occured reading data. Exiting")
import sys
sys.exit()
def getListFromDataframe(self, key, column):
return self.df[key][self.df[key].Status == \
'Include'][column].tolist()
def getDictFromDataframe(self, key, columns, df_override=None):
if isinstance(df_override, pd.DataFrame):
df_temp = df_override
else:
df_temp = self.df[key]
if len(columns) == 2:
return dict(df_temp[df_temp.Status == \
'Include'][columns].values)
elif len(columns) == 3:
returndict = {}
uniqueval = set(self.getListFromDataframe(
key, columns[0])
)
for val in uniqueval:
returndict[val] = self.getDictFromDataframe(
key, columns[1:],
df_temp[df_temp[columns[0]] == val])
return returndict
def processData(self):
'''Method reads information from dataframe and
converts into nicer data structure for ease of
use'''
# location IDs
self.customerID = self.getListFromDataframe(
'customers', 'CustomerID')
self.siteID = self.getListFromDataframe(
'sites', 'SiteID')
self.periodID = self.getListFromDataframe(
'periods', 'PeriodID')
# coordinates by location id
self.customerLat = self.getDictFromDataframe(
'customers', ['CustomerID', 'CustomerLatitude'])
self.customerLon = self.getDictFromDataframe(
'customers', ['CustomerID', 'CustomerLongitude'])
self.siteLat = self.getDictFromDataframe(
'sites', ['SiteID', 'SiteLatitude'])
self.siteLon = self.getDictFromDataframe(
'sites', ['SiteID', 'SiteLongitude'])
# site capacity in terms of max shipments per day
self.siteCapByPeriod = self.getDictFromDataframe(
'sitecapacity', ['PeriodID', 'SiteID', 'Capacity'])
# slack capacity on top of exiting capacity
self.siteSlackCapByPeriod = self.getDictFromDataframe(
'sitecapacity', ['PeriodID', 'SiteID', 'CapacitySlack'])
# customer demand, these are average shipments per day by cust id
self.customerDemByPeriod = self.getDictFromDataframe(
'demand', ['PeriodID', 'CustomerID', 'Demand'])
self.scenarioID = self.getListFromDataframe(
'scenarios', 'ScenarioID')
self.computeServiceDistances()
def computeServiceDistances(self):
'''Method computes distance between
facilities and customers using great
circle distance formula'''
self.serviceDist = {}
for sid in self.siteID:
lat1 = radians(self.siteLat[sid])
lon1 = radians(self.siteLon[sid])
for cid in self.customerID:
lat2 = radians(self.customerLat[cid])
lon2 = radians(self.customerLon[cid])
# radius of earth in miles - 3958.75
self.serviceDist[(sid, cid)] = round(3958.75 * (
acos(sin(lat1)*sin(lat2) \
+ cos(lat1)*cos(lat2)*cos(lon1-lon2))),
2)
def createFlows(self):
flm = FacilityLocationModel(
{
'periodid': self.periodID,
'siteid': self.siteID,
'customerid': self.customerID,
'sitecapbyperiod': self.siteCapByPeriod,
'siteslackcapbyperiod': self.siteSlackCapByPeriod,
'customerdembyperiod': self.customerDemByPeriod,
'servicedist': self.serviceDist,
'maxopensites': 4
}
)
flm.modelProblem()
flm.setParameters()
flm.solveModel()
flows, objectiveval = flm.extractSolution()
return flows, objectiveval
def solve(self):
flows, flowcost = self.createFlows()
flowrows = []
pathrows = []
clusters = []
routes = []
routepaths = []
scid = self.scenarioID[0]
for key, flow in flows.items():
if flow > 0:
pid, sid, cid = key
# flows
flowrows.append(
[scid, pid, sid, cid, flow,
self.serviceDist[sid, cid],
flowcost.get(key)]
)
# paths
pathid = "_".join([str(id_) for id_ in key])
pathrows.append(
[scid, pathid, pid, 'Site',
sid, self.siteLat[sid],
self.siteLon[sid], flow]
)
pathrows.append(
[scid, pathid, pid, 'Customer',
cid, self.customerLat[cid],
self.customerLon[cid], flow]
)
df_flow = CapEx.putInDataFrame(
flowrows, datafor='flows')
df_path = CapEx.putInDataFrame(
pathrows, datafor='flowpaths')
routeflows = RouteFlows(
self.siteLat, self.siteLon,
self.customerLat, self.customerLon,
self.siteID, self.customerID,
7, 700
)
for pid in self.periodID:
cluster_pid, route_pid, routepaths_pid = \
routeflows.createFlowRoutes(
df_flow[df_flow.PeriodID == pid],
pid, scid
)
clusters.extend(cluster_pid)
routes.extend(route_pid)
routepaths.extend(routepaths_pid)
break
df_clusters = CapEx.putInDataFrame(
clusters, datafor='clusters')
df_routes = CapEx.putInDataFrame(
routes, datafor='routes')
df_routepaths = CapEx.putInDataFrame(
routepaths, datafor='routepaths')
addUpdateSheet(scid, 'OutputClusters.xlsx',
{'Clusters': df_clusters})
addUpdateSheet(scid, 'OutputRoutes.xlsx',
{'Routes': df_routes})
addUpdateSheet(scid, 'OutputRoutePaths.xlsx',
{'RoutePaths': df_routepaths})
addUpdateSheet(scid, 'OutputData.xlsx',
{'OutputFlow': df_flow, 'OutputPath': df_path})
@staticmethod
def putInDataFrame(rows, datafor='flows'):
"""
Method to accept rows and create
pandas dataframe with columns based
on type of data. Removes messy list
of strings from core methods.
args:
rows: List of lists
Each list within represents
a row for require dataframe
kwargs:
datafor: str
Specify what data rows are for.
Must be among following list.
"""
columns_ = {
'flows': ['ScenarioID', 'PeriodID', 'SiteID',
'CustomerID', 'FlowUnits', 'Distance',
'ObjectiveValue'],
'flowpaths': ['ScenarioID', 'PathID', 'PeriodID',
'LocationType', 'LocationID', 'Latitude',
'Longitude', 'FlowUnits'],
'clusters': ['ScenarioID', 'PeriodID',
'SiteID', 'ClusterID', 'CustomerID',
'Count', 'Weight Arcs', 'Weight Nodes'],
'routes': ['ScenarioID', 'PeriodID',
'SiteID', 'RouteID', 'StopNumber',
'StopType', 'StopID', 'Distance',
'Cumulated Distance', 'LegType'],
'routepaths': ['ScenarioID', 'PeriodID',
'SiteID', 'RouteID', 'RouteKey',
'Latitude', 'Longitude', 'Distance']
}
if datafor in columns_:
try:
return pd.DataFrame(rows,
columns=columns_.get(datafor))
except Exception as e:
print("Something went wrong "+\
f"creating dataframe for {datafor}"
)
return pd.DataFrame([],
columns=columns_.get(datafor))
else:
raise KeyError("Unexpected data received")
return None
def addUpdateSheet(scid, file, sheetdfdict={}):
for sheet, df in sheetdfdict.items():
df.to_csv("test/"+str(scid)+str(file[:-5])+sheet+".csv", index=False)
"""
print(f"\nCalled to update {file}")
try:
wb = load_workbook(file)
print('Load Workbook Done .. ')
except IOError:
return
writer = pd.ExcelWriter(file,
engine='openpyxl')
print('Writer Object Created')
for sheet, df in sheetdfdict.items():
print(f'Updating sheet {sheet}')
if sheet not in wb.sheetnames:
df_sheet = df
else:
data = wb[sheet].values
df_sheet = pd.DataFrame(data, columns=next(data))
#df_sheet = pd.read_excel(
# pd.ExcelFile(file),
# sheet)
print("Finished reading data")
df_sheet = df_sheet[~(df_sheet.ScenarioID == scid)]
df_sheet = df_sheet.append(df, ignore_index=True)
print("Finished writing data")
wb.remove(wb[sheet])
writer.book = wb
df_sheet.to_excel(writer, sheet_name=sheet,
index=False)
writer.save()
writer.close()
print("Finished export")
"""
def test():
fcp = CapEx()
fcp.solve()
if __name__ == '__main__':
test()