Keywords
scenario analysis, optimisation under uncertainty, dynamic problems, reoptimisation
Start Date
1-7-2004 12:00 AM
Abstract
Many dynamic planning and management problems are typically characterised by a level ofuncertainty regarding the value of data input such as supply and demand patterns. Assigning inaccuratevalues to them could invalidate the results of the study. Consequently, deterministic models are inadequatefor the representation of these problems where the most crucial parameters are either unknown or are basedon an uncertain future. In these cases, the scenario analysis technique could be an alternative approach.Scenario analysis can model many real problems in which decisions are based on an uncertain future, whoseuncertainty is described by means of a set of possible future outcomes, called "scenarios". In this paper wepresent a scenario analysis approach to dynamic multi-period systems by integrating scenario optimisationand subsequent deterministic reoptimisation. In the scenario optimisation phase we represent data uncertaintyby a robust chance optimisation model obtaining a so-called barycentric value with respect to selecteddecision variables. The successive reoptimisation model based on this barycentric solution allows planning apart of the risk of a wrong decision, reducing the negative consequences deriving from it.
Scenario Reoptimisation under Data Uncertainty
Many dynamic planning and management problems are typically characterised by a level ofuncertainty regarding the value of data input such as supply and demand patterns. Assigning inaccuratevalues to them could invalidate the results of the study. Consequently, deterministic models are inadequatefor the representation of these problems where the most crucial parameters are either unknown or are basedon an uncertain future. In these cases, the scenario analysis technique could be an alternative approach.Scenario analysis can model many real problems in which decisions are based on an uncertain future, whoseuncertainty is described by means of a set of possible future outcomes, called "scenarios". In this paper wepresent a scenario analysis approach to dynamic multi-period systems by integrating scenario optimisationand subsequent deterministic reoptimisation. In the scenario optimisation phase we represent data uncertaintyby a robust chance optimisation model obtaining a so-called barycentric value with respect to selecteddecision variables. The successive reoptimisation model based on this barycentric solution allows planning apart of the risk of a wrong decision, reducing the negative consequences deriving from it.
