Linear fractional programming for fuzzy random based possibilistic programming problem

Nureize Arbaiy, Junzo Watada

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)


    The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguity. These uncertainties exist in the problem description and in the preference information in the mathematical programming model. Handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem is difficult to solve. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multi-objective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights its effectiveness.

    Original languageEnglish
    Pages (from-to)26-32
    Number of pages7
    JournalInternational Journal of Simulation: Systems, Science and Technology
    Issue number1
    Publication statusPublished - 2014


    • Component
    • Fractional programming
    • Fuzzy random data
    • Possibilistic programming
    • Vagueness and ambiguity

    ASJC Scopus subject areas

    • Software
    • Modelling and Simulation


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