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SensitivityAnalysis_01.py
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SensitivityAnalysis_01.py
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############################################################################
# Model for Optimizing Vehicle Routing problems with
# Perishable Products under Time-window and Quality Requirements
############################################################################
############ Importing Required Python Libraries ############
import json, datetime, time
from ModelFile import *
import matplotlib.pyplot as mpl
import numpy as np
# Import the json file
file_no = input('Dataset file number (after \"_\"): ')
json_file = open('DataSets\\dataset_'+file_no+'.json')
data = json.load(json_file)
############### Result File Generation ################
now = datetime.now() # current date and time
new_folder = 'Results\\Sensitivity_'+file_no+'_'+now.strftime("%m%d_%H%M%S")
if not os.path.exists(new_folder): os.mkdir(new_folder)
result_file = new_folder+'\\sensitivity_dataset'+file_no+'_'+now.strftime("%m%d_%H%M%S")
file_name = new_folder+'\\Results for '+file_no+' node Dataset'
runtime_min = 60
N = 10
IsFeasible = [0]*N
CurrObjVal = [0]*N
Veh_No_Used = [[0 for x in range(len(data['Vehicles']))] for y in range(N)]
## Run Original Model
og_model = VRP_Model(data,file_name,runtime_min)
og_C_q = float(data['QualityPenaltyCost'])
og_C_t = float(data['TimePenaltyCost'])
og_C_d = float(data['PerDriverCost'])
C_q_array = [450, 500, 550, 600, 650, 700, 750,1000]
C_t_array = [30, 35, 40, 45, 50, 55, 60,120]
C_d_array = [300, 325, 350, 375, 400, 425, 450,900]
def column(matrix, i):
return [row[i] for row in matrix]
def PlotSave(X_tag,Y_tag, plot_type,grid_on = True):
mpl.title(Y_tag+' v/s '+ X_tag)
mpl.grid(grid_on)
mpl.legend(loc = 'best')
mpl.xlabel(X_tag)
mpl.ylabel(Y_tag)
mpl.savefig(result_file+'_'+plot_type+'.png')
mpl.close()
def QualityPenaltyCost_Sensitivity():
with open(result_file+'_quality_penalty_cost.txt','w') as res:
print(f'--------------- Quality Penalty Cost Sensitivity --------------',file=res)
Index = len(C_q_array)
x_axis = np.arange(Index)
for iter in range(Index):
C_q = C_q_array[iter]
if C_q==og_C_q:
CurrObjVal[iter] = og_model.CurrObjVal
IsFeasible[iter] = og_model.IsFeasible
Veh_No_Used[iter] = og_model.VehiclesUsed
else:
data['QualityPenaltyCost'] = C_q
model = VRP_Model(data,file_name+'_Cq'+str(C_q),runtime_min)
CurrObjVal[iter] = model.CurrObjVal
IsFeasible[iter] = model.IsFeasible
Veh_No_Used[iter] = model.VehiclesUsed
if model.RunTime>1500: time.sleep(120)
print(f'C_q = {C_q}',file=res)
print(f'IsFeasible = {IsFeasible[iter]}',file=res)
print(f'CurrObjVal = {CurrObjVal[iter]}',file=res)
print(f'Vehicles Used = {Veh_No_Used[iter]}','\n',file=res)
mpl.plot(C_q_array,CurrObjVal[0:Index],'r--*')
mpl.plot(og_C_q, og_model.CurrObjVal,'g^',label='Original')
PlotSave('Quality Penalty Cost','Objective', 'quality_penalty_cost')
mpl.bar(x_axis-0.2,column(Veh_No_Used[0:Index],0),width = 0.2, label = 'Veh0')
mpl.bar(x_axis,column(Veh_No_Used[0:Index],1),width = 0.2, label = 'Veh1')
mpl.bar(x_axis+0.2,column(Veh_No_Used[0:Index],2),width = 0.2, label = 'Veh2')
mpl.xticks(x_axis, C_q_array)
PlotSave('Quality Penalty Cost','Vehicles Used', 'quality_veh_no',False)
## Restoring C_q
data['QualityPenaltyCost'] = og_C_q
def TimePenaltyCost_Sensitivity():
with open(result_file+'_Time_Penalty_Cost.txt','w') as res:
print(f'--------------- Time Penalty Cost Sensitivity --------------',file=res)
Index = len(C_t_array)
x_axis = np.arange(Index)
for iter in range(Index):
C_t = C_t_array[iter]
if C_t==og_C_t:
CurrObjVal[iter] = og_model.CurrObjVal
IsFeasible[iter] = og_model.IsFeasible
Veh_No_Used[iter] = og_model.VehiclesUsed
else:
data['TimePenaltyCost'] = C_t
model = VRP_Model(data,file_name+'_Ct'+str(C_t),runtime_min)
CurrObjVal[iter] = model.CurrObjVal
IsFeasible[iter] = model.IsFeasible
Veh_No_Used[iter] = model.VehiclesUsed
if model.RunTime>1500: time.sleep(120)
print(f'C_t = {C_t}',file=res)
print(f'IsFeasible = {IsFeasible[iter]}',file=res)
print(f'CurrObjVal = {CurrObjVal[iter]}',file=res)
print(f'Vehicles Used = {Veh_No_Used[iter]}','\n',file=res)
mpl.plot(C_t_array,CurrObjVal[0:Index],'b--*')
mpl.plot(og_C_t, og_model.CurrObjVal,'r^',label = 'Original')
#mpl.ylim((min(CurrObjVal[0:Index])-100),max(CurrObjVal)+100)
PlotSave('Time Penalty Cost','Objective', 'Time_Penalty_Cost')
mpl.bar(x_axis-0.2,column(Veh_No_Used[0:Index],0),width = 0.2, label = 'Veh0')
mpl.bar(x_axis,column(Veh_No_Used[0:Index],1),width = 0.2, label = 'Veh1')
mpl.bar(x_axis+0.2,column(Veh_No_Used[0:Index],2),width = 0.2, label = 'Veh2')
mpl.xticks(x_axis, C_t_array)
PlotSave('Time Penalty Cost','Vehicles Used', 'timePenalty_veh_no',False)
## Restoring C_t
data['TimePenaltyCost'] = og_C_t
def DriverCost_Sensitivity():
with open(result_file+'_Driver_Cost.txt','w') as res:
print(f'--------------- Driver Cost Sensitivity --------------',file=res)
Index = len(C_d_array)
x_axis = np.arange(Index)
for iter in range(Index):
C_d = C_d_array[iter]
if C_d==og_C_d:
CurrObjVal[iter] = og_model.CurrObjVal
IsFeasible[iter] = og_model.IsFeasible
Veh_No_Used[iter] = og_model.VehiclesUsed
else:
data['PerDriverCost'] = C_d
model = VRP_Model(data,file_name+'_Cd'+str(C_d),runtime_min)
CurrObjVal[iter] = model.CurrObjVal
IsFeasible[iter] = model.IsFeasible
Veh_No_Used[iter] = model.VehiclesUsed
if model.RunTime>1500: time.sleep(120)
print(f'C_d = {C_d}',file=res)
print(f'IsFeasible = {IsFeasible[iter]}',file=res)
print(f'CurrObjVal = {CurrObjVal[iter]}',file=res)
print(f'Vehicles Used = {Veh_No_Used[iter]}','\n',file=res)
mpl.plot(C_d_array,CurrObjVal[0:Index],'b--*')
mpl.plot(og_C_d, og_model.CurrObjVal,'r^',label = 'Original')
PlotSave('Driver Cost','Objective', 'Driver_Cost')
mpl.bar(x_axis-0.2,column(Veh_No_Used[0:Index],0),width = 0.2, label = 'Veh0')
mpl.bar(x_axis,column(Veh_No_Used[0:Index],1),width = 0.2, label = 'Veh1')
mpl.bar(x_axis+0.2,column(Veh_No_Used[0:Index],2),width = 0.2, label = 'Veh2')
mpl.xticks(x_axis, C_d_array)
PlotSave('Driver Cost','Vehicles Used', 'driver_veh_no', False)
## Restoring C_d
data['PerDriverCost'] = og_C_d
QualityPenaltyCost_Sensitivity()
TimePenaltyCost_Sensitivity()
DriverCost_Sensitivity()