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Information-Gap Decision Theory: Principles and Fundamentals

  • Navid Rezaei
  • Abdollah AhmadiEmail author
  • Ali Esmaeel Nezhad
  • Amirhossein Khazali
Chapter

Abstract

Solving optimization problems with multiple uncertainties has always been a challenging task in different scopes of science. While different approaches have been developed to take advantage of the stochastic space of the problem, these methods are intensively dependent of the probabilistic information of various variables which are not always available. Relying on the severity of the failure, information-gap decision theory (IGDT) is a robust optimization approach which is entirely autonomous from probabilistic information. In this model, a forecasted amount is presumed for each uncertain variable, and the sensitivity of objective functions is analyzed according to the deviation of each of these uncertain parameters from their forecasted value. In this method, two main types of uncertainty set models including energy-bound model and envelope-bound model are handled. In this chapter, these principles and fundamentals of IGDT are described.

Keywords

Information-gap decision theory (IGDT) Robust optimization Uncertainty Stochastic variables 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Navid Rezaei
    • 1
  • Abdollah Ahmadi
    • 2
    Email author
  • Ali Esmaeel Nezhad
    • 3
  • Amirhossein Khazali
    • 4
  1. 1.Electrical Engineering DepartmentUniversity of KurdistanSanandajIran
  2. 2.School of Electrical Engineering and TelecommunicationUniversity of New South WalesSydneyAustralia
  3. 3.Department of Electrical, Electronic, and Information EngineeringUniversity of BolognaBolognaItaly
  4. 4.Electrical Engineering DepartmentIran University of Science and TechnologyTehranIran

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