Reduction methods of type-2 uncertain variables and their applications to solid transportation problem

Lixing Yang*, Pei Liu, Shukai Li, Yuan Gao, Dan A. Ralescu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)

Abstract

Uncertainty theory is a branch of mathematics for dealing with realistic uncertainties arising out of complexity, changeability, and non-decidability of practical environments. An uncertain variable is defined as a function from the uncertainty space to the set of real numbers and is characterized by an uncertainty distribution. This paper proposes the definition of type-2 uncertain variables within the framework of uncertainty theory through introduction of generalized uncertain measures and focuses on more complex twofold uncertainties. Some uncertainty reduction methods associated with type-2 uncertain variables are also proposed for convenience of applicability, including reduction of optimistic value, pessimistic value and expected value. Moreover, four classes of type-2 uncertain variables are reduced to type-1 uncertain variables with specific uncertainty distributions. Type-2 uncertain optimization methods are applied to solving the fixed charge solid transportation problem with the type-2 uncertain parameters, where the solution methods are also provided for the proposed models. Finally, numerical experiments are implemented to demonstrate application and sensitivity analysis of the proposed approaches.

Original languageEnglish
Pages (from-to)204-237
Number of pages34
JournalInformation Sciences
Volume291
Issue numberC
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • Generalized uncertainty space
  • Reduction method
  • Solid transportation problem
  • Type-2 uncertain variable

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