A two-phase fuzzy approach for solving multi-level decision-making problems

Chen, Liang-Hsuan; Chen, Hsin-Hung

doi:10.1016/j.knosys.2014.12.016

Cited by 21 publications

(4 citation statements)

References 37 publications

(152 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the past few years, a number of methods had been given by many researchers in solving BLMP [3,17]. Sakawa [15] introduced interactive fuzzy programming for two level linear fractional programming problems.…”

Section: Ml-mofqfommentioning

confidence: 99%

FGP in Multi-objective Non-Linear Fractional Optimization Model including Triangular Fuzzy Numbers

Gupta¹,

Kumar²,

Rani³

2019

IJITEE

View full text Add to dashboard Cite

The motivation behind this paper is to propose method to obtain compromized solution of Non-Linear fractional Optimization Model. In this paper, Multi-level multi-objective fully quadratic fractional optimization model (ML-MOFQFOM) is studied in which various objective functions are involved, generally have conflicting nature. FGP approach is being used to solve ML-MOFQFOM involving triangular fuzzy numbers. This paper deals with the ML-MOFQFOM in which fuzzy model converted into deterministic form through the help of  -cuts where  is the combined choice of all objective functions. An algorithm and examples are also presented to validate the proposed method.

show abstract

Section: Ml-mofqfommentioning

confidence: 99%

FGP in Multi-objective Non-Linear Fractional Optimization Model including Triangular Fuzzy Numbers

Gupta¹,

Kumar²,

Rani³

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…A more extended model [57] has been proposed to include the weight values of the experts, which also vary with the criteria or attributes. Assigning the appropriate weight for each expert is a very significant step in MAGDM, and thus evaluation of experts' weights in GDM problems has been intensively investigated, and several methodologies, like TOPSIS [86,11], linear programming [12], or OWA [14,90], have been well developed.…”

Section: Group Decision Makingmentioning

confidence: 99%

Using mutually validated memories of experts for case-based knowledge systems

2015

Knowledge-Based Systems

View full text Add to dashboard Cite

“…Chen and Chen in [4] present a two-phase fuzzy goal programming approach to solve ML linear programming problems with fuzzy parameters in objective functions. Osman et al in [17] propose a new approach for solving the ML multi-objective fractional programming problems with rough intervals of the coefficients in the objective functions.…”

Section: Introductionmentioning

confidence: 99%

Solving multi level multi-objective linear programming (ML MOLP) problems with fuzzy parameters (FPs)

Perić,

Babić,

Hunjak

2023

Croat. oper. res. rev. (Online)

View full text Add to dashboard Cite

Making numerous business decisions in complex decentralized organizations can be presented as a ML MOLP problem with FPs expressed as triangular fuzzy numbers. This paper presents a methodology that uses several multi-objective programming methods to solve this problem. The Iskander’s method was used for defuzzification of the objective functions and constraints of the problem and a multi-objective programming method based on the cooperation among decision makers was used to determine the aspired values of the variables controlled by the decision-makers and to obtain the preferred non-dominated solution of the entire problem. The efficiency of the proposed methodology was tested on an example of production planning in a complex decentralized company.

show abstract

A two-phase fuzzy approach for solving multi-level decision-making problems

Cited by 21 publications

References 37 publications

FGP in Multi-objective Non-Linear Fractional Optimization Model including Triangular Fuzzy Numbers

FGP in Multi-objective Non-Linear Fractional Optimization Model including Triangular Fuzzy Numbers

Using mutually validated memories of experts for case-based knowledge systems

Solving multi level multi-objective linear programming (ML MOLP) problems with fuzzy parameters (FPs)

Contact Info

Product

Resources

About