Risk management of CPM networks under randomness and fuzziness based on conditional Value-at-Risk

T. Hasuike*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper considers an extended critical path method (CPM) in terms of risk management to analyze the criticality in a network with both randomness and fuzziness for activities duration times. In order to formulate the risk management mathematically, a new risk measure is proposed by synthesizing both the conditional Value-at-Risk (cVaR) for randomness and the order relation for fuzziness. The proposed model defined by the integrated risk measure is transformed into a linear programming problem using scenario simulation approach for randomness. Furthermore, in the case of normal distributions, the proposed model is transformed into an existing parametric linear programming problem, and so the strict solution algorithm is also developed.

Original languageEnglish
Title of host publication21st International Conference on Production Research
Subtitle of host publicationInnovation in Product and Production, ICPR 2011 - Conference Proceedings
EditorsTobias Krause, Dieter Spath, Rolf Ilg
PublisherFraunhofer-Verlag
ISBN (Electronic)9783839602935
Publication statusPublished - 2011 Jan 1
Externally publishedYes
Event21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Stuttgart, Germany
Duration: 2011 Jul 312011 Aug 4

Publication series

Name21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings

Other

Other21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011
Country/TerritoryGermany
CityStuttgart
Period11/7/3111/8/4

Keywords

  • Conditional Value-at-Risk (cVaR)
  • Critical path method (CPM)
  • Fuzziness
  • Randomness
  • Risk management

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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