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270ajay/0VRPTWBigM.py

Last active July 26, 2018 07:20
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Optimizing Vehicle Routing Problem with Time Windows using Mixed Integer Programming (BigM formulation). Uses PuLP library in python.
Raw
0VRPTWBigM.py
import csv
import pulp #This contains LP Modeling and solver
import time #This is used to see how much time for solving
#######################################################################
#######################################################################
NumberOfPoints = 10 # number of points/locations in the data
k = 5 # number of vehicles
BigM = 50
StartTime = 12 #12 means 6am. 14 means 8am. half hour is 1 time unit.
EndTime = 44 #44 means 22:00 or 10pm.
ObjectiveWeightForVehicles = [50,100,150,200,250] #length of this list should be equal to k (number of vehicles)
#######################################################################
#######################################################################
assert len(ObjectiveWeightForVehicles) == k #checking if that condition satisfies.
#------------------------------------------------------------------------------------------------------
# GETTING & PREPARING DATA
#------------------------------------------------------------------------------------------------------
vehicleList = range(k)
Points = range(1,NumberOfPoints+1)
Nodes = range(0,NumberOfPoints+2) #there are 21 nodes as 0 and numberofpoints+1 are entry and exit depots respectively.
with open('travelTimes.csv') as csvfile:
data = list(csv.reader(csvfile))
travelTimes = {}
for i in Points:
for j in Points:
if data[j][i].isdigit() and i!=j:
travelTimes[(i,j)] = float(int(data[j][i])/10)
#Addition of 0 and NumberOfPoints+1 points as they are depots in this model.
travelTimes[(0,NumberOfPoints+1)] = 0
for i in Points:
travelTimes[(0,i)] = 0
travelTimes[(i,(NumberOfPoints+1))] = 0
#Addition of symmetrical travel times
for i in range(2,NumberOfPoints+1):
for j in range(1,i):
travelTimes[(i,j)] = travelTimes[(j,i)]
travelFrom,travelTo = zip(*list(travelTimes.keys()))
#-----------------------------------------------------------------------------
with open('locations.csv') as csvfile1:
data1 = list(csv.reader(csvfile1))
LocationData = dict([(point,[]) for point in Points])
for j in [1,6,7]:
for i in Points:
LocationData[i].append(data1[i][j])
CapacityOfVehicle = float(data1[1][4])
#------------------------------------------------------------------------------------------------------
# LP MODELING
#------------------------------------------------------------------------------------------------------
VRP = pulp.LpProblem("Assign_FromPointId_ToPointId_VehicleId, StartTime(S)_PointId_VehicleId", pulp.LpMinimize)
DecisionVar = pulp.LpVariable.dicts("Assign",(Nodes,Nodes,vehicleList),0,1,pulp.LpInteger) #Binary
TimeDecisionVar = pulp.LpVariable.dicts("Start",(Nodes,vehicleList),0,None, pulp.LpContinuous)
for point in Points:
for vehicle in vehicleList:
TimeDecisionVar[point][vehicle].bounds(float(LocationData[point][1]), float(LocationData[point][2]))
VRP += sum([float(travelTimes[From,To]) * ObjectiveWeightForVehicles[vehicle] * DecisionVar[From][To][vehicle] for From,To in zip(travelFrom,travelTo)
for vehicle in vehicleList])
#Objective (2.1 in mip model.png)
for point in Points:
VRP += pulp.lpSum([DecisionVar[point][node][vehicle] for node in Nodes
for vehicle in vehicleList if point!=node]) == 1, "eachCustomerOnce"+str(point)
#constraint1 (2.2 in mip model.png)
for vehicle in vehicleList:
VRP += pulp.lpSum([float(LocationData[point][0]) * DecisionVar[point][node][vehicle] for point in Points
for node in Nodes if point!=node]) <= float(CapacityOfVehicle), "Capacity"+str(vehicle)
#constraint2 (2.3 in mip model.png)
for vehicle in vehicleList:
VRP += pulp.lpSum([DecisionVar[0][node][vehicle] for node in
Nodes if node!=0]) == 1, "entryDepotConnection"+str(vehicle)
#constraint3 (2.4 in mip model.png)
#there are 21 nodes as 0 and NumberOfPoints+1 are entry and exit depots respectively.
for vehicle in vehicleList:
VRP += pulp.lpSum([DecisionVar[node][NumberOfPoints+1][vehicle] for node in Nodes
if node!=NumberOfPoints+1]) == 1, "exitDepotConnection"+str(vehicle)
#constraint4 (2.6 in mip model.png)
for point in Points:
for vehicle in vehicleList:
VRP += pulp.lpSum([DecisionVar[node][point][vehicle] - DecisionVar[point][node][vehicle] for node in
Nodes if node!=point]) == 0, "forTrip"+str(point)+'k'+str(vehicle)
#constraint5 (2.5 in mip model.png)
for point in Points:
for vehicle in vehicleList:
VRP += DecisionVar[point][0][vehicle] == 0, "ToEntryDepot"+str(point)+str(vehicle)
VRP += DecisionVar[NumberOfPoints+1][point][vehicle] == 0, "FromExitDepot"+str(point)+str(vehicle)
#constraint6 - For constraint5 to work correctly.
for vehicle in vehicleList:
for node1 in Nodes:
if node1 == NumberOfPoints+1:
continue
else:
for node2 in Nodes:
if node2 == 0:
continue
else:
if (node1 != node2):
VRP += TimeDecisionVar[node1][vehicle] - TimeDecisionVar[node2][vehicle]\
+ BigM * DecisionVar[node1][node2][vehicle] <= float(BigM - travelTimes[node1,node2]), \
"timewindow"+str(vehicle)+'p'+str(node1)+'p'+str(node2)
for vehicle in vehicleList:
VRP += TimeDecisionVar[NumberOfPoints+1][vehicle] <= EndTime, "EndTime"+str(vehicle)
VRP += TimeDecisionVar[0][vehicle] >= StartTime, "StartTime"+str(vehicle)
VRP.writeLP("VRPLPModel.lp")
tic = time.time()
VRP.solve()
toc = time.time()
print("Time taken for solving is " + str(1000*(toc-tic)) + "ms")
print("")
for vehicle in vehicleList:
for node1 in Nodes:
for node2 in Nodes:
if DecisionVar[node1][node2][vehicle].value() == 1.0:
print("Point:"+ str(node1)+ " to Point:" + str(node2) + " by vehicle:" + str(vehicle))
print("Objective value:",pulp.value(VRP.objective))
print("See VRPOutput.csv and VRPLPModel.lp for more details.")
#------------------------------------------------------------------------------------------------------
# PRINTING RESULTS INTO CSV
#------------------------------------------------------------------------------------------------------
with open("VRPOutput.csv","w",newline='') as file:
datawriter = csv.writer(file)
datawriter.writerow(['PointTravelled','vehicle',"",'DecisionVar=1','TimeDecisionVar','StartTime'])
for vehicle in vehicleList:
for node1 in Nodes:
for node2 in Nodes:
if DecisionVar[node1][node2][vehicle].value() == 1.0:
#if node1 != 0: #to not print activity0.
datawriter.writerow(['Point'+str(node1),str(vehicle+1),"",str(DecisionVar[node1][node2][vehicle]),
str(TimeDecisionVar[node1][vehicle]),str(TimeDecisionVar[node1][vehicle].value())])
Raw
1VRPTWSampleLPFile.lp
\* Assign_FromPointId_ToPointId_VehicleId, StartTime(S)_PointId_VehicleId *\
Minimize
OBJ: 35 Assign_1_2_0 + 70 Assign_1_2_1 + 40 Assign_1_3_0 + 80 Assign_1_3_1
+ 65 Assign_1_4_0 + 130 Assign_1_4_1 + 25 Assign_1_5_0 + 50 Assign_1_5_1
+ 35 Assign_2_1_0 + 70 Assign_2_1_1 + 60 Assign_2_3_0 + 120 Assign_2_3_1
+ 80 Assign_2_4_0 + 160 Assign_2_4_1 + 30 Assign_2_5_0 + 60 Assign_2_5_1
+ 40 Assign_3_1_0 + 80 Assign_3_1_1 + 60 Assign_3_2_0 + 120 Assign_3_2_1
+ 45 Assign_3_4_0 + 90 Assign_3_4_1 + 35 Assign_3_5_0 + 70 Assign_3_5_1
+ 65 Assign_4_1_0 + 130 Assign_4_1_1 + 80 Assign_4_2_0 + 160 Assign_4_2_1
+ 45 Assign_4_3_0 + 90 Assign_4_3_1 + 45 Assign_4_5_0 + 90 Assign_4_5_1
+ 25 Assign_5_1_0 + 50 Assign_5_1_1 + 30 Assign_5_2_0 + 60 Assign_5_2_1
+ 35 Assign_5_3_0 + 70 Assign_5_3_1 + 45 Assign_5_4_0 + 90 Assign_5_4_1
Subject To
Capacity0: 33 Assign_1_0_0 + 33 Assign_1_2_0 + 33 Assign_1_3_0
+ 33 Assign_1_4_0 + 33 Assign_1_5_0 + 33 Assign_1_6_0 + 47 Assign_2_0_0
+ 47 Assign_2_1_0 + 47 Assign_2_3_0 + 47 Assign_2_4_0 + 47 Assign_2_5_0
+ 47 Assign_2_6_0 + 10 Assign_3_0_0 + 10 Assign_3_1_0 + 10 Assign_3_2_0
+ 10 Assign_3_4_0 + 10 Assign_3_5_0 + 10 Assign_3_6_0 + 25 Assign_4_0_0
+ 25 Assign_4_1_0 + 25 Assign_4_2_0 + 25 Assign_4_3_0 + 25 Assign_4_5_0
+ 25 Assign_4_6_0 + 34 Assign_5_0_0 + 34 Assign_5_1_0 + 34 Assign_5_2_0
+ 34 Assign_5_3_0 + 34 Assign_5_4_0 + 34 Assign_5_6_0 <= 200
Capacity1: 33 Assign_1_0_1 + 33 Assign_1_2_1 + 33 Assign_1_3_1
+ 33 Assign_1_4_1 + 33 Assign_1_5_1 + 33 Assign_1_6_1 + 47 Assign_2_0_1
+ 47 Assign_2_1_1 + 47 Assign_2_3_1 + 47 Assign_2_4_1 + 47 Assign_2_5_1
+ 47 Assign_2_6_1 + 10 Assign_3_0_1 + 10 Assign_3_1_1 + 10 Assign_3_2_1
+ 10 Assign_3_4_1 + 10 Assign_3_5_1 + 10 Assign_3_6_1 + 25 Assign_4_0_1
+ 25 Assign_4_1_1 + 25 Assign_4_2_1 + 25 Assign_4_3_1 + 25 Assign_4_5_1
+ 25 Assign_4_6_1 + 34 Assign_5_0_1 + 34 Assign_5_1_1 + 34 Assign_5_2_1
+ 34 Assign_5_3_1 + 34 Assign_5_4_1 + 34 Assign_5_6_1 <= 200
EndTime0: Start_6_0 <= 44
EndTime1: Start_6_1 <= 44
FromExitDepot10: Assign_6_1_0 = 0
FromExitDepot11: Assign_6_1_1 = 0
FromExitDepot20: Assign_6_2_0 = 0
FromExitDepot21: Assign_6_2_1 = 0
FromExitDepot30: Assign_6_3_0 = 0
FromExitDepot31: Assign_6_3_1 = 0
FromExitDepot40: Assign_6_4_0 = 0
FromExitDepot41: Assign_6_4_1 = 0
FromExitDepot50: Assign_6_5_0 = 0
FromExitDepot51: Assign_6_5_1 = 0
StartTime0: Start_0_0 >= 12
StartTime1: Start_0_1 >= 12
ToEntryDepot10: Assign_1_0_0 = 0
ToEntryDepot11: Assign_1_0_1 = 0
ToEntryDepot20: Assign_2_0_0 = 0
ToEntryDepot21: Assign_2_0_1 = 0
ToEntryDepot30: Assign_3_0_0 = 0
ToEntryDepot31: Assign_3_0_1 = 0
ToEntryDepot40: Assign_4_0_0 = 0
ToEntryDepot41: Assign_4_0_1 = 0
ToEntryDepot50: Assign_5_0_0 = 0
ToEntryDepot51: Assign_5_0_1 = 0
eachCustomerOnce1: Assign_1_0_0 + Assign_1_0_1 + Assign_1_2_0 + Assign_1_2_1
+ Assign_1_3_0 + Assign_1_3_1 + Assign_1_4_0 + Assign_1_4_1 + Assign_1_5_0
+ Assign_1_5_1 + Assign_1_6_0 + Assign_1_6_1 = 1
eachCustomerOnce2: Assign_2_0_0 + Assign_2_0_1 + Assign_2_1_0 + Assign_2_1_1
+ Assign_2_3_0 + Assign_2_3_1 + Assign_2_4_0 + Assign_2_4_1 + Assign_2_5_0
+ Assign_2_5_1 + Assign_2_6_0 + Assign_2_6_1 = 1
eachCustomerOnce3: Assign_3_0_0 + Assign_3_0_1 + Assign_3_1_0 + Assign_3_1_1
+ Assign_3_2_0 + Assign_3_2_1 + Assign_3_4_0 + Assign_3_4_1 + Assign_3_5_0
+ Assign_3_5_1 + Assign_3_6_0 + Assign_3_6_1 = 1
eachCustomerOnce4: Assign_4_0_0 + Assign_4_0_1 + Assign_4_1_0 + Assign_4_1_1
+ Assign_4_2_0 + Assign_4_2_1 + Assign_4_3_0 + Assign_4_3_1 + Assign_4_5_0
+ Assign_4_5_1 + Assign_4_6_0 + Assign_4_6_1 = 1
eachCustomerOnce5: Assign_5_0_0 + Assign_5_0_1 + Assign_5_1_0 + Assign_5_1_1
+ Assign_5_2_0 + Assign_5_2_1 + Assign_5_3_0 + Assign_5_3_1 + Assign_5_4_0
+ Assign_5_4_1 + Assign_5_6_0 + Assign_5_6_1 = 1
entryDepotConnection0: Assign_0_1_0 + Assign_0_2_0 + Assign_0_3_0
+ Assign_0_4_0 + Assign_0_5_0 + Assign_0_6_0 = 1
entryDepotConnection1: Assign_0_1_1 + Assign_0_2_1 + Assign_0_3_1
+ Assign_0_4_1 + Assign_0_5_1 + Assign_0_6_1 = 1
exitDepotConnection0: Assign_0_6_0 + Assign_1_6_0 + Assign_2_6_0
+ Assign_3_6_0 + Assign_4_6_0 + Assign_5_6_0 = 1
exitDepotConnection1: Assign_0_6_1 + Assign_1_6_1 + Assign_2_6_1
+ Assign_3_6_1 + Assign_4_6_1 + Assign_5_6_1 = 1
forTrip1k0: Assign_0_1_0 - Assign_1_0_0 - Assign_1_2_0 - Assign_1_3_0
- Assign_1_4_0 - Assign_1_5_0 - Assign_1_6_0 + Assign_2_1_0 + Assign_3_1_0
+ Assign_4_1_0 + Assign_5_1_0 + Assign_6_1_0 = 0
forTrip1k1: Assign_0_1_1 - Assign_1_0_1 - Assign_1_2_1 - Assign_1_3_1
- Assign_1_4_1 - Assign_1_5_1 - Assign_1_6_1 + Assign_2_1_1 + Assign_3_1_1
+ Assign_4_1_1 + Assign_5_1_1 + Assign_6_1_1 = 0
forTrip2k0: Assign_0_2_0 + Assign_1_2_0 - Assign_2_0_0 - Assign_2_1_0
- Assign_2_3_0 - Assign_2_4_0 - Assign_2_5_0 - Assign_2_6_0 + Assign_3_2_0
+ Assign_4_2_0 + Assign_5_2_0 + Assign_6_2_0 = 0
forTrip2k1: Assign_0_2_1 + Assign_1_2_1 - Assign_2_0_1 - Assign_2_1_1
- Assign_2_3_1 - Assign_2_4_1 - Assign_2_5_1 - Assign_2_6_1 + Assign_3_2_1
+ Assign_4_2_1 + Assign_5_2_1 + Assign_6_2_1 = 0
forTrip3k0: Assign_0_3_0 + Assign_1_3_0 + Assign_2_3_0 - Assign_3_0_0
- Assign_3_1_0 - Assign_3_2_0 - Assign_3_4_0 - Assign_3_5_0 - Assign_3_6_0
+ Assign_4_3_0 + Assign_5_3_0 + Assign_6_3_0 = 0
forTrip3k1: Assign_0_3_1 + Assign_1_3_1 + Assign_2_3_1 - Assign_3_0_1
- Assign_3_1_1 - Assign_3_2_1 - Assign_3_4_1 - Assign_3_5_1 - Assign_3_6_1
+ Assign_4_3_1 + Assign_5_3_1 + Assign_6_3_1 = 0
forTrip4k0: Assign_0_4_0 + Assign_1_4_0 + Assign_2_4_0 + Assign_3_4_0
- Assign_4_0_0 - Assign_4_1_0 - Assign_4_2_0 - Assign_4_3_0 - Assign_4_5_0
- Assign_4_6_0 + Assign_5_4_0 + Assign_6_4_0 = 0
forTrip4k1: Assign_0_4_1 + Assign_1_4_1 + Assign_2_4_1 + Assign_3_4_1
- Assign_4_0_1 - Assign_4_1_1 - Assign_4_2_1 - Assign_4_3_1 - Assign_4_5_1
- Assign_4_6_1 + Assign_5_4_1 + Assign_6_4_1 = 0
forTrip5k0: Assign_0_5_0 + Assign_1_5_0 + Assign_2_5_0 + Assign_3_5_0
+ Assign_4_5_0 - Assign_5_0_0 - Assign_5_1_0 - Assign_5_2_0 - Assign_5_3_0
- Assign_5_4_0 - Assign_5_6_0 + Assign_6_5_0 = 0
forTrip5k1: Assign_0_5_1 + Assign_1_5_1 + Assign_2_5_1 + Assign_3_5_1
+ Assign_4_5_1 - Assign_5_0_1 - Assign_5_1_1 - Assign_5_2_1 - Assign_5_3_1
- Assign_5_4_1 - Assign_5_6_1 + Assign_6_5_1 = 0
timewindow0p0p1: 50 Assign_0_1_0 + Start_0_0 - Start_1_0 <= 50
timewindow0p0p2: 50 Assign_0_2_0 + Start_0_0 - Start_2_0 <= 50
timewindow0p0p3: 50 Assign_0_3_0 + Start_0_0 - Start_3_0 <= 50
timewindow0p0p4: 50 Assign_0_4_0 + Start_0_0 - Start_4_0 <= 50
timewindow0p0p5: 50 Assign_0_5_0 + Start_0_0 - Start_5_0 <= 50
timewindow0p0p6: 50 Assign_0_6_0 + Start_0_0 - Start_6_0 <= 50
timewindow0p1p2: 50 Assign_1_2_0 + Start_1_0 - Start_2_0 <= 49.3
timewindow0p1p3: 50 Assign_1_3_0 + Start_1_0 - Start_3_0 <= 49.2
timewindow0p1p4: 50 Assign_1_4_0 + Start_1_0 - Start_4_0 <= 48.7
timewindow0p1p5: 50 Assign_1_5_0 + Start_1_0 - Start_5_0 <= 49.5
timewindow0p1p6: 50 Assign_1_6_0 + Start_1_0 - Start_6_0 <= 50
timewindow0p2p1: 50 Assign_2_1_0 - Start_1_0 + Start_2_0 <= 49.3
timewindow0p2p3: 50 Assign_2_3_0 + Start_2_0 - Start_3_0 <= 48.8
timewindow0p2p4: 50 Assign_2_4_0 + Start_2_0 - Start_4_0 <= 48.4
timewindow0p2p5: 50 Assign_2_5_0 + Start_2_0 - Start_5_0 <= 49.4
timewindow0p2p6: 50 Assign_2_6_0 + Start_2_0 - Start_6_0 <= 50
timewindow0p3p1: 50 Assign_3_1_0 - Start_1_0 + Start_3_0 <= 49.2
timewindow0p3p2: 50 Assign_3_2_0 - Start_2_0 + Start_3_0 <= 48.8
timewindow0p3p4: 50 Assign_3_4_0 + Start_3_0 - Start_4_0 <= 49.1
timewindow0p3p5: 50 Assign_3_5_0 + Start_3_0 - Start_5_0 <= 49.3
timewindow0p3p6: 50 Assign_3_6_0 + Start_3_0 - Start_6_0 <= 50
timewindow0p4p1: 50 Assign_4_1_0 - Start_1_0 + Start_4_0 <= 48.7
timewindow0p4p2: 50 Assign_4_2_0 - Start_2_0 + Start_4_0 <= 48.4
timewindow0p4p3: 50 Assign_4_3_0 - Start_3_0 + Start_4_0 <= 49.1
timewindow0p4p5: 50 Assign_4_5_0 + Start_4_0 - Start_5_0 <= 49.1
timewindow0p4p6: 50 Assign_4_6_0 + Start_4_0 - Start_6_0 <= 50
timewindow0p5p1: 50 Assign_5_1_0 - Start_1_0 + Start_5_0 <= 49.5
timewindow0p5p2: 50 Assign_5_2_0 - Start_2_0 + Start_5_0 <= 49.4
timewindow0p5p3: 50 Assign_5_3_0 - Start_3_0 + Start_5_0 <= 49.3
timewindow0p5p4: 50 Assign_5_4_0 - Start_4_0 + Start_5_0 <= 49.1
timewindow0p5p6: 50 Assign_5_6_0 + Start_5_0 - Start_6_0 <= 50
timewindow1p0p1: 50 Assign_0_1_1 + Start_0_1 - Start_1_1 <= 50
timewindow1p0p2: 50 Assign_0_2_1 + Start_0_1 - Start_2_1 <= 50
timewindow1p0p3: 50 Assign_0_3_1 + Start_0_1 - Start_3_1 <= 50
timewindow1p0p4: 50 Assign_0_4_1 + Start_0_1 - Start_4_1 <= 50
timewindow1p0p5: 50 Assign_0_5_1 + Start_0_1 - Start_5_1 <= 50
timewindow1p0p6: 50 Assign_0_6_1 + Start_0_1 - Start_6_1 <= 50
timewindow1p1p2: 50 Assign_1_2_1 + Start_1_1 - Start_2_1 <= 49.3
timewindow1p1p3: 50 Assign_1_3_1 + Start_1_1 - Start_3_1 <= 49.2
timewindow1p1p4: 50 Assign_1_4_1 + Start_1_1 - Start_4_1 <= 48.7
timewindow1p1p5: 50 Assign_1_5_1 + Start_1_1 - Start_5_1 <= 49.5
timewindow1p1p6: 50 Assign_1_6_1 + Start_1_1 - Start_6_1 <= 50
timewindow1p2p1: 50 Assign_2_1_1 - Start_1_1 + Start_2_1 <= 49.3
timewindow1p2p3: 50 Assign_2_3_1 + Start_2_1 - Start_3_1 <= 48.8
timewindow1p2p4: 50 Assign_2_4_1 + Start_2_1 - Start_4_1 <= 48.4
timewindow1p2p5: 50 Assign_2_5_1 + Start_2_1 - Start_5_1 <= 49.4
timewindow1p2p6: 50 Assign_2_6_1 + Start_2_1 - Start_6_1 <= 50
timewindow1p3p1: 50 Assign_3_1_1 - Start_1_1 + Start_3_1 <= 49.2
timewindow1p3p2: 50 Assign_3_2_1 - Start_2_1 + Start_3_1 <= 48.8
timewindow1p3p4: 50 Assign_3_4_1 + Start_3_1 - Start_4_1 <= 49.1
timewindow1p3p5: 50 Assign_3_5_1 + Start_3_1 - Start_5_1 <= 49.3
timewindow1p3p6: 50 Assign_3_6_1 + Start_3_1 - Start_6_1 <= 50
timewindow1p4p1: 50 Assign_4_1_1 - Start_1_1 + Start_4_1 <= 48.7
timewindow1p4p2: 50 Assign_4_2_1 - Start_2_1 + Start_4_1 <= 48.4
timewindow1p4p3: 50 Assign_4_3_1 - Start_3_1 + Start_4_1 <= 49.1
timewindow1p4p5: 50 Assign_4_5_1 + Start_4_1 - Start_5_1 <= 49.1
timewindow1p4p6: 50 Assign_4_6_1 + Start_4_1 - Start_6_1 <= 50
timewindow1p5p1: 50 Assign_5_1_1 - Start_1_1 + Start_5_1 <= 49.5
timewindow1p5p2: 50 Assign_5_2_1 - Start_2_1 + Start_5_1 <= 49.4
timewindow1p5p3: 50 Assign_5_3_1 - Start_3_1 + Start_5_1 <= 49.3
timewindow1p5p4: 50 Assign_5_4_1 - Start_4_1 + Start_5_1 <= 49.1
timewindow1p5p6: 50 Assign_5_6_1 + Start_5_1 - Start_6_1 <= 50
Bounds
12 <= Start_1_0 <= 18
12 <= Start_1_1 <= 18
12 <= Start_2_0 <= 18
12 <= Start_2_1 <= 18
12 <= Start_3_0 <= 18
12 <= Start_3_1 <= 18
12 <= Start_4_0 <= 18
12 <= Start_4_1 <= 18
18 <= Start_5_0 <= 24
18 <= Start_5_1 <= 24
Binaries
Assign_0_1_0
Assign_0_1_1
Assign_0_2_0
Assign_0_2_1
Assign_0_3_0
Assign_0_3_1
Assign_0_4_0
Assign_0_4_1
Assign_0_5_0
Assign_0_5_1
Assign_0_6_0
Assign_0_6_1
Assign_1_0_0
Assign_1_0_1
Assign_1_2_0
Assign_1_2_1
Assign_1_3_0
Assign_1_3_1
Assign_1_4_0
Assign_1_4_1
Assign_1_5_0
Assign_1_5_1
Assign_1_6_0
Assign_1_6_1
Assign_2_0_0
Assign_2_0_1
Assign_2_1_0
Assign_2_1_1
Assign_2_3_0
Assign_2_3_1
Assign_2_4_0
Assign_2_4_1
Assign_2_5_0
Assign_2_5_1
Assign_2_6_0
Assign_2_6_1
Assign_3_0_0
Assign_3_0_1
Assign_3_1_0
Assign_3_1_1
Assign_3_2_0
Assign_3_2_1
Assign_3_4_0
Assign_3_4_1
Assign_3_5_0
Assign_3_5_1
Assign_3_6_0
Assign_3_6_1
Assign_4_0_0
Assign_4_0_1
Assign_4_1_0
Assign_4_1_1
Assign_4_2_0
Assign_4_2_1
Assign_4_3_0
Assign_4_3_1
Assign_4_5_0
Assign_4_5_1
Assign_4_6_0
Assign_4_6_1
Assign_5_0_0
Assign_5_0_1
Assign_5_1_0
Assign_5_1_1
Assign_5_2_0
Assign_5_2_1
Assign_5_3_0
Assign_5_3_1
Assign_5_4_0
Assign_5_4_1
Assign_5_6_0
Assign_5_6_1
Assign_6_1_0
Assign_6_1_1
Assign_6_2_0
Assign_6_2_1
Assign_6_3_0
Assign_6_3_1
Assign_6_4_0
Assign_6_4_1
Assign_6_5_0
Assign_6_5_1
End
Raw
2locations.csv
Location Demand TimeIn TimeOut ServiceTime
Point1 23 Vehicle Capacity 200 12 18 2
Point2 24 12 18 1
Point3 17 12 18 2
Point4 25 12 18 1
Point5 13 18 24 1
Point6 29 18 24 1
Point7 24 18 24 2
Point8 25 18 24 1
Point9 26 18 24 2
Point10 22 18 24 2
Point11 11 26 32 1
Point12 16 26 32 2
Point13 25 26 32 1
Point14 23 30 36 2
Point15 13 30 36 1
Point16 18 36 42 2
Point17 23 36 42 1
Point18 21 36 42 1
Point19 15 36 42 1
Point20 20 36 42 2
Point21 13 12 18 1
Point22 20 12 18 1
Point23 15 12 18 1
Point24 12 12 18 1
Point25 18 18 24 1
Point26 12 18 24 1
Point27 27 18 24 1
Point28 10 18 24 2
Point29 14 18 24 2
Point30 17 18 24 1
Point31 26 26 32 2
Point32 16 26 32 1
Point33 10 26 32 2
Point34 30 30 38 2
Point35 23 30 38 2
Point36 16 36 42 2
Point37 30 36 42 2
Point38 28 36 42 2
Point39 14 36 42 2
Point40 22 36 42 1
Point41 30 26 32 1
Point42 16 26 32 1
Point43 29 26 32 1
Point44 17 30 36 1
Point45 13 30 36 1
Point46 22 36 42 1
Point47 21 36 42 2
Point48 14 36 42 1
Point49 18 36 42 1
Point50 13 36 42 1
Raw
3travelTimes.csv
driveTime Point1 Point2 Point3 Point4 Point5 Point6 Point7 Point8 Point9 Point10 Point11 Point12 Point13 Point14 Point15 Point16 Point17 Point18 Point19 Point20 Point21 Point22 Point23 Point24 Point25 Point26 Point27 Point28 Point29 Point30 Point31 Point32 Point33 Point34 Point35 Point36 Point37 Point38 Point39 Point40 Point41 Point42 Point43 Point44 Point45 Point46 Point47 Point48 Point49 Point50
Point1 0
Point2 7 0
Point3 8 12 0
Point4 13 16 9 0
Point5 5 6 7 9 0
Point6 8 12 15 19 17 0
Point7 20 20 7 15 13 8 0
Point8 8 9 20 17 10 12 16 0
Point9 18 5 17 13 17 13 13 12 0
Point10 9 19 9 6 6 18 11 6 8 0
Point11 8 8 15 13 15 14 14 9 17 10 0
Point12 5 6 6 17 18 5 10 7 17 15 14 0
Point13 10 13 8 11 11 20 10 13 13 9 12 19 0
Point14 10 18 14 15 6 18 11 8 6 7 14 10 8 0
Point15 19 9 7 14 8 14 13 7 19 8 6 9 10 14 0
Point16 6 14 13 16 18 16 12 13 13 15 9 6 12 13 10 0
Point17 11 11 8 18 7 8 15 9 10 5 12 18 16 14 7 19 0
Point18 5 9 6 13 8 14 6 8 17 11 11 13 8 12 14 9 18 0
Point19 14 9 7 19 17 19 14 9 13 9 9 10 16 11 6 16 20 13 0
Point20 15 16 9 13 7 15 7 19 9 19 8 13 18 18 10 20 13 17 7 0
Point21 17 10 8 8 20 14 7 19 12 17 6 10 12 11 9 10 9 5 8 9 0
Point22 7 11 18 7 16 8 18 13 7 11 19 9 8 13 5 12 16 17 6 6 6 0
Point23 14 5 13 10 14 11 19 11 20 19 16 13 9 6 17 15 16 5 13 13 9 19 0
Point24 19 15 13 18 19 17 14 19 11 11 14 11 11 15 14 8 15 14 18 5 17 19 19 0
Point25 9 7 5 11 7 18 9 5 9 10 15 6 6 6 15 16 5 11 6 5 12 9 17 16 0
Point26 10 20 16 13 10 16 14 13 18 12 14 11 16 13 9 5 8 6 6 11 6 11 14 20 14 0
Point27 12 8 16 16 15 10 8 18 14 10 16 16 20 13 14 15 10 12 6 14 13 18 13 13 11 10 0
Point28 20 8 11 19 15 19 10 14 9 19 12 11 10 15 15 12 15 9 8 17 9 8 8 20 7 7 16 0
Point29 5 14 9 17 15 10 17 6 10 15 19 13 15 20 15 6 9 6 16 12 15 19 7 16 15 7 10 5 0
Point30 13 13 18 11 8 20 18 19 12 9 15 8 19 16 20 17 20 16 17 17 20 13 8 5 19 6 9 12 15 0
Point31 20 9 8 10 10 7 15 13 14 8 5 12 17 20 19 14 9 14 7 13 20 10 18 17 14 19 8 6 8 6 0
Point32 6 7 6 6 19 13 18 17 12 13 7 16 18 13 17 11 13 13 9 18 18 17 16 16 15 12 15 11 13 18 14 0
Point33 15 6 5 7 19 19 6 11 10 18 19 18 19 15 20 17 10 7 13 8 18 16 16 8 8 8 10 13 5 11 7 16 0
Point34 8 5 18 10 10 7 10 6 6 18 17 19 19 11 6 19 13 16 19 12 15 17 20 19 7 7 19 14 18 10 14 20 11 0
Point35 5 16 10 15 9 6 9 14 8 10 14 6 16 6 7 9 8 15 20 8 19 7 12 9 16 15 13 10 10 7 16 14 7 15 0
Point36 19 14 19 18 14 7 8 16 6 12 19 8 11 16 17 10 12 8 8 20 8 10 18 5 17 6 9 16 8 20 5 10 18 9 19 0
Point37 11 8 14 16 13 8 8 18 8 18 15 9 7 18 19 8 11 15 5 17 9 18 17 9 5 13 7 5 8 18 15 18 5 17 13 11 0
Point38 5 17 15 16 12 12 14 6 20 15 10 8 15 17 9 8 8 10 20 9 18 13 6 20 6 12 19 8 5 8 8 10 18 7 11 11 13 0
Point39 12 18 9 19 17 14 13 10 15 17 7 7 14 19 5 13 7 5 6 13 12 11 6 10 20 15 17 11 15 15 13 11 6 15 15 6 8 11 0
Point40 19 14 9 14 20 7 19 12 19 6 18 8 18 18 20 12 12 17 14 12 19 13 9 11 6 17 16 9 11 14 7 11 11 15 12 19 10 17 5 0
Point41 6 15 8 6 17 7 7 17 11 7 19 6 13 20 12 17 6 12 15 10 8 18 20 10 9 13 14 8 20 16 5 17 19 6 16 18 5 19 14 14 0
Point42 10 15 11 9 6 19 19 15 13 6 18 19 8 15 12 19 18 11 18 20 17 15 9 16 12 11 14 19 7 12 10 14 13 17 6 5 10 18 17 7 20 0
Point43 13 6 7 16 16 14 11 18 18 15 10 14 11 17 5 14 9 6 15 20 13 19 10 10 15 10 8 11 5 12 18 6 20 13 5 15 20 8 14 5 9 8 0
Point44 5 12 5 15 17 16 6 10 5 6 5 8 9 11 19 20 18 6 8 20 12 16 20 6 17 7 7 8 16 10 14 13 20 13 20 14 19 14 11 10 12 7 15 0
Point45 18 11 6 17 6 9 19 8 12 9 9 10 17 13 13 17 18 17 6 12 18 10 16 18 18 5 10 20 14 10 9 10 18 16 12 17 9 13 10 7 5 7 17 16 0
Point46 13 9 11 9 14 17 18 10 10 8 20 9 7 20 8 20 20 19 7 7 8 12 19 14 15 16 17 7 18 7 16 15 10 19 9 9 7 16 17 7 12 14 18 9 6 0
Point47 6 11 14 17 14 9 10 18 6 9 19 18 8 8 15 20 16 12 8 11 11 6 14 20 14 19 11 6 8 20 8 19 11 9 16 10 13 10 19 20 12 9 15 16 5 18 0
Point48 14 18 18 12 10 9 6 14 14 10 10 18 14 18 10 14 12 13 20 19 14 7 8 8 12 7 18 7 14 16 12 12 8 14 20 9 15 11 17 12 13 16 19 15 18 13 8 0
Point49 9 18 20 13 13 13 12 17 14 16 19 12 11 18 12 20 19 6 12 19 17 16 12 6 7 13 7 15 5 20 5 17 7 13 10 15 14 13 14 18 15 11 18 10 11 17 17 18 0
Point50 19 14 11 17 12 10 5 15 18 10 17 19 9 19 7 15 11 15 7 18 15 18 13 20 8 18 20 7 10 5 15 8 8 12 18 11 6 18 15 14 5 20 16 12 12 6 5 11 12 0
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