def Optimal_Data(self, c_list, R_list, thres_CR):
self.c_list = c_list
self.R_list = R_list
self.thres_CR = thres_CR
res = pd.DataFrame()
print('Generate optimal test plans data for ' + self.name + ' RDT')
cR_list = np.array([(c, R) for c in self.c_list for R in self.R_list])
CR_list = []
#PR_list = []
AP_list = []
RDT_list = []
RG_list = []
RG_exp_list = []
WS_list = []
WS_exp_list = []
WS_failureprob_list = []
cost_list = []
n_optimal_list = []
for i in range(len(cR_list)):
self.c = cR_list[i][0]
self.R = cR_list[i][1]
self.n = Optimal.Optimal(self.name, self.pi).Optimal_Sample_Size(self.c, self.R, self.thres_CR)
n_optimal_list.append(self.n)
CR_list.append(Risk.Risk(self.name, n = self.n, c = self.c, pi = self.pi, R = self.R).Consumer_Risk())
#PR_list.append(Risk.Risk(self.name, n = self.n, c = self.c, pi = self.pi, R = self.R).Producer_Risk())
AP_list.append(Risk.Risk(self.name, n = self.n, c = self.c, pi = self.pi, R = self.R).Acceptance_Prob())
RDT_list.append(Cost.Cost(self.name, n = self.n, c = self.c, pi = self.pi, R = self.R).RDT(self.cost_fix, self.cost_var))
RG_list.append(Cost.Cost(self.name, n = self.n, c = self.c, pi = self.pi, R = self.R).Reliability_Growth(self.cost_reliability_growth))
RG_exp_list.append(RG_list[i] * (1 - AP_list[i]))
WS = Cost.Cost(self.name, n = self.n, c = self.c, pi = self.pi, R = self.R).Warranty(self.sales_volume, self. cost_warranty)
WS_list.append(WS[0])
WS_failureprob_list.append(WS[1])
WS_exp_list.append(WS_list[i] * AP_list[i])
cost_list.append(RDT_list[i] + RG_exp_list[i] + WS_exp_list[i])
res['n'] = n_optimal_list
res['c'] = cR_list[:, 0]
res['R'] = cR_list[:, 1]
res['CR'] = CR_list
#res['PR'] = PR_list
res['AP'] = AP_list
res['RDT_Cost'] = RDT_list
res['Reliabiltiy_Growth_Cost'] = RG_list
res['Reliabiltiy_Growth_Cost_exp'] = RG_exp_list
res['Warranty_Services_Cost'] = WS_list
res['Warranty_Services_Failure_Probability'] = WS_failureprob_list
res['Warranty_Services_Cost_exp'] = WS_exp_list
res['Cost_exp'] = cost_list
return res
def evaluate(self, solution):
y = solution.variables
solution.objectives[:] = [
-Res.Res(y, self.d, self.hStar),
Cost.Cost(y, self.d)
]
solution.constraints[:] = Constraint.Constraint(y, self.d, self.hStar)
def fitness(self, x):
from epanettools import epanet2 as et
from epanettools.epanettools import EPANetSimulation, Node, Link, Network, Nodes, \
Links, Patterns, Pattern, Controls, Control # import all elements needed
d = EPANetSimulation('/home/varsha/Documents/Project.inp')
f1 = Cost.Cost(x)
f2 = PRI.PRI(x, d)
return [f1, f2]
def evaluate(self, solution):
y = solution.variables
solution.objectives[:] = [PRI.PRI(y, self.d), Cost.Cost(y, self.d)]
solution.constraints[:] = Constraint.Constraint(y, self.d)
def buildCostMap(self, testMap):
cost = Cost()
costMap = cost.buildCostMap(testMap)
return costMap
