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M.K. Luhandjula and M. Sakawa, “Multiple-objective linear programming problems in the presence of fuzzy coefficients”, In R. Slowinski and J. Teghem, editors, Stochastic versus fuzzy approaches to multiobjective mathematical programming under uncertainty. Kluwer Academic Publishers, 1990.

has been cited by the following article:

• TITLE: Multiobjective Stochastic Linear Programming: An Overview

  AUTHORS: A. Segun Adeyefa, Monga K. Luhandjula

  KEYWORDS: Linear Programming, Multiobjective Programming, Stochastic Programming, Expected Value Optimality/Efficiency, Minimum Risk Solution/Efficiency, Variance Optimality/Efficiency, Optimality/Efficiency with Given Probabilities.

  JOURNAL NAME: American Journal of Operations Research, Vol.1 No.4, December 5, 2011

  ABSTRACT: Many Optimization problems in engineering and economic involve the challenging task of pondering both conflicting goals and random data. In this paper, we give an up-to-date overview of how important ideas from optimization, probability theory and multicriteria decision analysis are interwoven to address situations where the presence of several objective functions and the stochastic nature of data are under one roof in a linear optimization context. In this way users of these models are not bound to caricature their problems by arbitrarily squeezing different objective functions into one and by blindly accepting fixed values in lieu of imprecise ones.