In this paper, we discuss the evaluation of quality/suitability of scenario-generation methods for a given stochastic programming model. We formulate minimal requirements that should be imposed on a scenario-generation method before it can be used for solving the stochastic pro-gramming model. We also show how the requirements can be tested.The procedure of testing a scenario-generation method is illustrated on a case from portfolio management. In addition, we provide a short overview of the most common scenario-generation methods
Scenario tree generation methods are powerful decision-making tools when decisions have to be made u...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
In this paper, we discuss the evaluation of quality/suitability of scenario-generation methods for a...
In this paper, we discuss the evaluation of quality/suitability of scenario-generation methods for a...
Stochastic programs can only be solved with discrete distributions of limited cardinality. Input, ho...
In recent years, stochastic programming has gained an increasing popularity within the mathematical ...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
In stochastic programming models we always face the problem of how to represent the random variables...
In stochastic programming models we always face the problem of how to represent the random variables...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
Stochastic programming concerns mathematical programming in the presence of uncertainty. In a stocha...
This work presents an empirical analysis of popular scenario generation methods for stochastic optim...
Scenario tree generation methods are powerful decision-making tools when decisions have to be made u...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
In this paper, we discuss the evaluation of quality/suitability of scenario-generation methods for a...
In this paper, we discuss the evaluation of quality/suitability of scenario-generation methods for a...
Stochastic programs can only be solved with discrete distributions of limited cardinality. Input, ho...
In recent years, stochastic programming has gained an increasing popularity within the mathematical ...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
In stochastic programming models we always face the problem of how to represent the random variables...
In stochastic programming models we always face the problem of how to represent the random variables...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
The field of multi-stage stochastic programming provides a rich modelling framework to tackle a broa...
Stochastic programming concerns mathematical programming in the presence of uncertainty. In a stocha...
This work presents an empirical analysis of popular scenario generation methods for stochastic optim...
Scenario tree generation methods are powerful decision-making tools when decisions have to be made u...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...
Scenario generation is the construction of a discrete random vector to represent parameters of uncer...