def pysp_scenario_tree_model_callback(self):
"""
scenario tree instance creation callback
@ In, None
@ Out, treeModel, Instance, pyomo scenario tree model for two stage stochastic programming,
extra variables 'y[*,*]' is used to define the priorities of investments,
'gamma' and 'nu[*]' is used for the distributionally robust optimization counter-part.
"""
treeModel = MCKP.pysp_scenario_tree_model_callback(self)
# additional variables:
firstStage = treeModel.Stages.first()
treeModel.StageVariables[firstStage].add('gamma')
treeModel.StageVariables[firstStage].add('nu[*]')
return treeModel
def pysp_scenario_tree_model_callback(self):
"""
scenario tree instance creation callback
@ In, None
@ Out, treeModel, Instance, pyomo scenario tree model for two stage stochastic programming,
extra variables 'y[*,*]' is used to define the priorities of investments,
'u' and 'nu' is used for CVaR optimization counter-part.
'cvar' is used to retrieve the calculated CVaR value
'expectProfit' is used to retrieve the expect profit under CVaR optimization
"""
treeModel = MCKP.pysp_scenario_tree_model_callback(self)
# additional variables:
firstStage = treeModel.Stages.first()
secondStage = treeModel.Stages.last()
treeModel.StageVariables[firstStage].add('u')
treeModel.StageVariables[secondStage].add('nu')
treeModel.StageVariables[secondStage].add('cvar')
treeModel.StageVariables[secondStage].add('expectProfit')
return treeModel