Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm

Çiçek, Kadir; Celik, Metin

doi:10.1016/j.matdes.2009.11.016

Cited by 53 publications

(18 citation statements)

References 21 publications

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“…Some recent applications of FAD prin ciples in the field of decisionmaking can be summarized as follows. Cicek and Celik (2010) solved material selection problems using a modified FAD model. Boran et al (2012) evaluated various energy policies for Turkey using FAD principles.…”

Section: Fad Principlesmentioning

confidence: 99%

Personnel Selection Using Fuzzy Axiomatic Design Principles

Khandekar

Chakraborty

2016

Business: Theory and Practice

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Overall competency of the working personnel is often observed to ultimately affect the productivity of an organization. The globalised competitive atmosphere coupled with technological improvements demands for efficient and specialized manpower for the industrial operations. A set of typical technological skills and attitudes is thus demanded for every job profile. Most often, these skills and attitudes are expressed imprecisely and hence, necessitating the support of fuzzy sets for their effective understanding and further processing. In this paper, a method based on fuzzy axiomatic design principles is applied for solving the personnel selection problems. Selecting a middle management staff of a service department for a large scale organization is demonstrated here as a real life example. Five shortlisted candidates are assessed with respect to a set of 18 evaluation criteria, and the selection committee with experts from the related fields also realizes the outcome of the adopted approach to be quite appropriate, befitting and in agreement with their expectations.

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Section: Fad Principlesmentioning

confidence: 99%

Personnel Selection Using Fuzzy Axiomatic Design Principles

Khandekar

Chakraborty

2016

Business: Theory and Practice

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“…Previous researchers have proposed several mathematical tools and techniques (including MCDM methods), such as graph theory and matrix approach [2], analytic hierarchy process [3], distance evaluation algorithm [4], preference selection index method [5], preference ranking organization method for enrichment evaluation (PROMETHEE) [6], preference ranking-based methods [7 -9], utility additive method [10], utility concept [11], weighted property index method [12], linear assignment method [13], quality function deployment [14], modified fuzzy axiomatic design method [15], integrated subjective and objective methods [16], multi-objective optimization using ratio analysis (MOORA) method [17], and desirability function [11] for solving diverse material selection problems. However, the most widely employed MCDM methods for materials selection are TOPSIS (technique for order preference by similarity to ideal solution) [18 -23], VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) [22, 24 -27], ELECTRE (elimination and choice expressing the reality) [24, 28 -31], grey relational analysis (GRA) [32 -34], Ztransformation [35,36] and digital logic [37,38].…”

Section: Introductionmentioning

confidence: 99%

“…However, the most widely employed MCDM methods for materials selection are TOPSIS (technique for order preference by similarity to ideal solution) [18 -23], VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) [22, 24 -27], ELECTRE (elimination and choice expressing the reality) [24, 28 -31], grey relational analysis (GRA) [32 -34], Ztransformation [35,36] and digital logic [37,38]. Some of J. Babu et al: Application of the grey-based fuzzy logic approach for materials selection these methods either adopt fuzzy logic or are applied under fuzzy environment for materials selection [15,20,38]. Past researchers have also applied grey fuzzy logic concept so as to optimize multiple objectives of various manufacturingrelated problems and concluded that it can significantly improve the performance of the manufacturing processes [39 -43].…”

Section: Introductionmentioning

confidence: 99%

Application of the grey-based fuzzy logic approach for materials selection

Babu

James

Philip

et al. 2017

International Journal of Materials Research

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Material selection is considered an important step in the design and manufacturing of a part/product. Wrong selection of material may cause poor quality, increase in cost, early failure of the product, or any combination of them. It may also create difficulties in the manufacturing process leading to increases in manufacturing time and cost. The general rule in design is to select materials based on their properties that match with the product requirements. Hence, decision making for material selection is a crucial process, and several mathematical tools and techniques have been presented in the literature to aid this process. Those techniques have their own advantages and limitations. This paper proposes the application of the grey-based fuzzy logic approach to solve three real time material selection problems. For all the examples, the first and the last ranks of materials derived using the adopted method and those obtained by past researchers are exactly the same. This indicates that when uncertainties are involved, grey-based fuzzy logic can be employed as an efficient tool for solving complex material selection problems.

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“…FAD also provides the opportunity to evaluate design concepts with a combination of qualitative and quantitative design criteria ( [1]). The application trends of FAD have focused on design review ( [16]), manufacturing system evaluation ( [30]), equipment selection ( [26]), assessment of transportation companies ( [31]), seat evaluation considering usability factors ( [13]), proposing competitive strategies on Turkish container ports ( [5]), model selection for ship management ( [6]), docking performance of shipyards ( [7]), material selection ( [11], [12]), teaching assistant selection ( [20]), ship design ( [4]), selection of renewable energy alternative ( [24], [22]), concept selection for design ( [1]), green supplier selection ( [3]), and ergonomic advanced manufacturing technology selection ( [34]). These studies have demonstrated the applicability and benefits of FAD especially for solving selection problems in fuzzy environments.…”

Section: Introductionmentioning

confidence: 99%