def addAdditionalConstraints(self, model):
"""
Add specific constraints for DROMKP problems
@ In, model, pyomo model instance, pyomo abstract model
@ Out, model, pyomo model instance, pyomo abstract model
"""
model = MultipleKnapsack.addAdditionalConstraints(self, model)
## model.dist will be changed on the fly
def constraintWasserstein(model, i):
expr = sum(
sum(model.net_present_values[i] * model.x[i, m]
for i in model.investments) for m in model.capitals)
return -model.gamma * model.dist[i] + model.nu[i] <= expr
model.constraintWassersteinDistance = pyomo.Constraint(
model.sigma, rule=constraintWasserstein)
return model
def addAdditionalConstraints(self, model):
"""
Add specific constraints for DROMKP problems
@ In, model, pyomo model instance, pyomo abstract model
@ Out, model, pyomo model instance, pyomo abstract model
"""
model = MultipleKnapsack.addAdditionalConstraints(self, model)
def cvarConstraint(model):
return model.nu + sum(
sum(model.net_present_values[i] * model.x[i, m]
for i in model.investments)
for m in model.capitals) + model.u >= 0
model.cvarConstraint = pyomo.Constraint(rule=cvarConstraint)
## used to retrieve additional information from the optimization
def expectProfit(model):
"""
Method to compute the expect profit of stochastic programming
@ In, model, instance, pyomo abstract model instance
@ Out, expr, pyomo.expression, constraint to compute the expect profit
"""
return model.expectProfit - sum(
sum(model.net_present_values[i] * model.x[i, m]
for i in model.investments) for m in model.capitals) == 0
model.computeExpectProfit = pyomo.Constraint(rule=expectProfit)
def cvar(model):
"""
Method to compute the conditional value at risk
@ In, model, instance, pyomo abstract model instance
@ Out, expr, pyomo.expression, constraint to compute the CVaR
"""
return model.cvar - model.u + 1. / (1. -
model.alpha) * model.nu == 0
model.computeCVAR = pyomo.Constraint(rule=cvar)
return model
