Interactive Sequential Fuzzy Goal Programming

Sato, Masato; Gen, Mitsuo; Ida, Kenichi

doi:10.1016/0360-8352(90)90182-l

Cited by 16 publications

(2 citation statements)

References 6 publications

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“…1(a) and (b)). The problem (25) and membership functions, (26) to (30), for five fuzzy goals are given below:…”

Section: Examplementioning

confidence: 99%

A modified augmented max min model for weighted fuzzy goal programming

Arikan

2014

Journal of Intelligent &Amp; Fuzzy Systems

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Since Narasimhan's first application of Fuzzy Set Theory to Goal Programming (GP) in 1980, much research into fuzzy GP has been carried out. In fuzzy GP studies, although several researchers remained loyal to using the traditional GP representation, the majority have followed the fuzzy programming approach. Among fuzzy-programming-based studies, only the studies of Tiwari et al. and Lin seem applicable when the objectives have relative importance. However, both have disadvantages. Because the former model uses the add operator, some of the objectives may not be preferred at the optimal solution even they have heavy weights. The latter model is based on the max-min approach, which does not guarantee a non-dominated solution. In this study, a weighted Fuzzy GP model is presented to overcome these disadvantages, along with the shortcomings of other existing models. The model is modified from Lai and Hwang's augmented max-min model, which is guaranteed to reach a non-dominated solution. The superiority of the model over the existing approaches is demonstrated using numerical examples chosen from the literature.

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“…1(a) and (b)). The problem (25) and membership functions, (26) to (30), for five fuzzy goals are given below:…”

Section: Examplementioning

confidence: 99%

A modified augmented max min model for weighted fuzzy goal programming

Arikan

2014

Journal of Intelligent &Amp; Fuzzy Systems

View full text Add to dashboard Cite

show abstract

“…Sasaki et al . (1990) have developed an interactive sequential FGP model to which the method of displaced ideal is applied to find the best compromised solution. Abd El‐Wahed and Lee (2006) have combined GP, fuzzy set theory and interactive approaches to develop an Interactive FGP for handling a multi‐objective transportation problem.…”

Section: The Main Variants Of the Gp Modelmentioning

confidence: 99%

Decision‐maker's preferences modelling within the goal‐programming model: a new typology

Aouni

Hassaine

Martel

2009

Multi Criteria Decision Anal

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Several classifications of the Multiple Objectives Programming (MOP) models have been proposed in the literature. In general, these classifications are based on the timing of introducing the decision-maker's (DM) preferences and the type of the required information about the parameters of the decision-making situation. The DM's preference information can take different forms such as: weights, priority levels, thresholds or trade-offs among the objectives. The Goal Programming (GP) is one of the well-known MOP models. The different GP formulations deal differently with the DM's preferences. The aim of this paper is to propose a new typology of the GP variants based on the way that the DM's preferences are considered.

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Fuzzy goal programming model: an overview of the current state‐of‐the art

Aouni

Martel

Hassaine

2009

Multi Criteria Decision Anal

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The standard Goal Programming (GP) model considers the aspiration levels (goals) as precise and deterministic. However, in practice, there are many decision-making situations where the decision-maker is not able to establish the goal values precisely. The goals fuzziness is more related to the nature of the objectives involved in the decisionmaking situation. The Fuzzy Goal Programming (FGP) Model has been developed in the earliest of the 80s to deal with such situations. The concept of membership functions, based on fuzzy sets theory, has been used for modelling the goals fuzziness in the GP. The aim of this paper is to give an overview of the current state-of-the art regarding the FGP model.

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Interactive Sequential Fuzzy Goal Programming

Cited by 16 publications

References 6 publications

A modified augmented max min model for weighted fuzzy goal programming

A modified augmented max min model for weighted fuzzy goal programming

Decision‐maker's preferences modelling within the goal‐programming model: a new typology

Fuzzy goal programming model: an overview of the current state‐of‐the art

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