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Fuzzy random minimum cost network problem [electronic resource]
, Article Journal of Industrial and Systems Engineering ; Vol. 6, No. 1, pp 34-47, 2012 ; Eshghi, Kourosh ; Sharif University of Technology
Abstract
In this paper, a fuzzy random minimum cost flow problem is presented. In this problem, cost parameters and decision variables are fuzzy random variables and fuzzy numbers respectively. The object of the problem is to find optimal flows of a capacitated network. Then, two algorithms are developed to solve the problem based on Er-expected value of fuzzy random variables and chance-constrained programming. Furthermore, the results of two algorithms will be compared. An illustrative example is also provided to clarify the concept
A resource constrained project scheduling problem with fuzzy random duration [electronic resource]
, Article Journal of Uncertain Systems ; Vol.4, No.2, pp.123-132, 2010 ; Eshghi, Kourosh ; Eshragh Jahromi, Abdolhamid ; Sharif University of Technology
Abstract
In this paper, first a fuzzy random resource-constrained project scheduling problem is presented. The object of the problem is to find the optimal scheduling of project activities. In this model, duration of project activities is a fuzzy random variable. Then, the proposed model is formulated by using the expected value of fuzzy random variables as an IP model. An illustrative example is also provided to clarify the concept. © 2010 World Academic Press, UK. All rights reserved
A fuzzy random minimum cost network flow programming problem [electronic resource]
, Article Journal of Industrial and Systems Engineering (JISE)-Iranian Institute of Industrial Engineering ; Article 3, Volume 6, Issue 1, Spring 2012, Page 34-47 ; Eshghi, Kourosh ; Sharif University of Technology
Abstract
In this paper, a fuzzy random minimum cost flow problem is presented. In this problem, cost parameters and decision variables are fuzzy random variables and fuzzy numbers respectively. The object of the problem is to find optimal flows of a capacitated network. Then, two algorithms are developed to solve the problem based on Er-expected value of fuzzy random variables and chance-constrained programming. Furthermore, the results of two algorithms will be compared. An illustrative example is also provided to clarify the concept