Fuzzy outranking for environmental assessment. Case study: iron and steel making industry

Geldermann, Jutta; Spengler, Thomas S.; Rentz, Otto

doi:10.1016/s0165-0114(99)00021-4

Cited by 277 publications

(154 citation statements)

References 18 publications

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“…Six different preference function types P j , were defined 12,21 and expressed by crisp values in the unit interval.…”

Section: Promethee-based Outranking Methodsmentioning

confidence: 99%

Pure linguistic PROMETHEE I and II methods for heterogeneous MCGDM problems

Espinilla¹,

Halouani²,

Chabchoub³

2015

IJCIS

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The PROMETHEE methods basic principle is focused on a pairwise comparison of alternatives for each criterion, selecting a preference function type that often requires parameters in order to obtain a preference value. When a MCGDM problem is defined in an heterogeneous context, an adequate and common approach is to unify the involved information in linguistic values. However, for each criterion, there is a difficulty to select the specific preference function and define its parameters because they are expressed by crisp values in the unit interval, when the information involved in the problem has been unified into linguistic values. In this paper, a methodology for modeling linguistic preference functions in order to facilitate the selecting of each linguistic preference function type and the definition of its parameters is proposed, providing a more realistic definition of the criteria. Therefore, a generic linguistic preference function is proposed whose inputs and outputs are linguistic values. According to the generic linguistic preference function, six basic preference function types are extended for linguistic values. To do so, a linguistic difference function between linguistic values is defined, being its output, the input of the linguistic preference function. Furthermore, the proposed methodology is integrated in linguistic PROMETHEE I and II for heterogeneous MCGDM problems to obtain partial rankings and a full ranking of alternatives. So, the methodology provides pure linguistic PROMETHEE I and II that offer interpretability and understandability. Finally, the feasibility and applicability of pure linguistic PROMETHEE I and II are illustrated in a case study for the selection of a green supplier.

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“…Six different preference function types P j , were defined 12,21 and expressed by crisp values in the unit interval.…”

Section: Promethee-based Outranking Methodsmentioning

confidence: 99%

Pure linguistic PROMETHEE I and II methods for heterogeneous MCGDM problems

Espinilla¹,

Halouani²,

Chabchoub³

2015

IJCIS

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“…(Zadeh, 1965;Zimmermann & Gutsche, 1991) A possible representation of a fuzzy number and the membership function is shown in figure 9. The formal notation for fuzzy numbers is displayed with a tilde over the literal (Chen, Hwang, & Hwang, 1992;Geldermann, Sprengler, & Rentz, 2000;Goumas & Lygerou, 2000). In this context the membership function of a fuzzy number (e. g. M  ) can be described using a middle value (m), a lower spread (α) and an upper spread (β), see equation 1.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…The operations used in the further algorithms are fuzzy addition ( ), fuzzy subtraction ( ), fuzzy multiplication ( ) and fuzzy division ( ) (Chen, Hwang, & Hwang, 1992;Geldermann, Sprengler, & Rentz, 2000).…”

Section: Notation Of a Triangular Fuzzy Numbermentioning

confidence: 99%

Learning to See Clear: Quantification and Multidimensional Assessment of Value Stream Mapping Alternatives Considering Variability

Roessler

Metternich

Abele

2014

BMR

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The prior quantification and validation of future state maps in lean production and optimization projects mostly is not taken into consideration in the traditional value stream mapping approaches. Furthermore the implementation of future states is based upon the trial and error principle. The effects of proactively changing production systems often are unknown and could underlie vast variations due to the planned outcome. So for many managers hard facts are missing and the uncertainties included in such a value stream optimization project are very high. This prevents a necessary system change accompanied by the adoption of lean methods. Thus in this paper a comprehensive value stream optimization approach is presented which primarily focuses upon chances for prior static and dynamic future state map quantification. Under consideration of parameter variability a downstream multidimensional assessment of possible design alternatives is proposed using a fuzzy decision making method to facilitate transparency in the selection of the most adequate future state map. The method described in this paper will be discussed at an industrial case study.

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“…The basic steps of the PROMETHEE algorithm can be outlined as follows (Brans et al, 1986;Geldermann et al, 2000):…”

Section: Promethee and F-prometheementioning

confidence: 99%

“…In the literature, there are a few studies using F-PROMETHEE approach. Bilsel et al (2006), Chou et al (2007), Geldermann et al (2000) and Goumas and Lygerou (2000) have used F-PROMETHEE previously.…”

Section: Promethee and F-prometheementioning

confidence: 99%

Environmental performance evaluation of suppliers: A hybrid fuzzy multi-criteria decision approach

Tuzkaya

Özgen

et al. 2009

Int. J. Environ. Sci. Technol.

175

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ABSTRACT:Increasing environmental problems enforce companies to be more environmental responsible.A company's environmental performance is not only related to the company's inner environmental efforts, but also it is affected by the suppliers' environmental performance and image. As a stakeholder of the company, a supplier with bad environmental image affects badly the company's environmental image. Also, raw materials and semi-finished products supplied from out of the company recourses-suppliers-affects the products environmental features such as life cycle, re-usability, re-manufacturability, hazardous substances, etc. Considering these direct and indirect effects, managers should also consider environmental performances of their suppliers in their supplier evaluation process. In this paper, a methodology for the evaluation of suppliers' environmental performances is proposed. In this methodology, a hybrid Fuzzy-Analytic Network Process and Fuzzy-Preference Ranking Organization METHod for Enrichment Evaluations approach is utilized. Additionally, a numerical example is given to foster the better understanding of the methodology and the obtained results are analyzed with sensitivity analyses.

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Fuzzy outranking for environmental assessment. Case study: iron and steel making industry

Cited by 277 publications

References 18 publications

Pure linguistic PROMETHEE I and II methods for heterogeneous MCGDM problems

Pure linguistic PROMETHEE I and II methods for heterogeneous MCGDM problems

Learning to See Clear: Quantification and Multidimensional Assessment of Value Stream Mapping Alternatives Considering Variability

Environmental performance evaluation of suppliers: A hybrid fuzzy multi-criteria decision approach

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