Multiattribute Evaluation of Organic and Inorganic Agricultural Food Investments Using Fuzzy Topsis

Suder, Asli; Kahraman, Cengiz

doi:10.3846/20294913.2016.1216905

Cited by 19 publications

(11 citation statements)

References 29 publications

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“…The usability of TOPSIS continues to motivate researchers of information and decision making sciences to further develop the method by novel approaches (Shen et al, 2018). It has been demonstrated that improved and extended versions of TOPSIS can tackle more complex problems than can conventional TOPSIS (Shouzhen andYao, 2018, Suder andKahraman, 2018).…”

Section: Brief Review Of Recent Developments In Topsismentioning

confidence: 99%

TOPSIS-RTCID for range target-based criteria and interval data

Jahan

Yazdani

Edwards

2021

Int. J. Prod. Manag. Eng.

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<p>The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is receiving considerable attention as an essential decision analysis technique and becoming a leading method. This paper describes a new version of TOPSIS with interval data and capability to deal with all types of criteria. An improved structure of the TOPSIS is presented to deal with high uncertainty in engineering and engineering decision-making. The proposed Range Target-based Criteria and Interval Data model of TOPSIS (TOPSIS-RTCID) achieves the core contribution in decision making theories through a distinct normalization formula for cost and benefits criteria in scale of point and range target-based values. It is important to notice a very interesting property of the proposed normalization formula being opposite to the usual one. This property can explain why the rank reversal problem is limited. The applicability of the proposed TOPSIS-RTCID method is examined with several empirical litreture’s examples with comparisons, sensitivity analysis, and simulation. The authors have developed a new tool with more efficient, reliable and robust outcomes compared to that from other available tools. The complexity of an engineering design decision problem can be resolved through the development of a well-structured decision making method with multiple attributes. Various decision approches developed for engineering design have neglected elements that should have been taken into account. Through this study, engineering design problems can be resolved with greater reliability and confidence.</p>

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Section: Brief Review Of Recent Developments In Topsismentioning

confidence: 99%

TOPSIS-RTCID for range target-based criteria and interval data

Jahan

Yazdani

Edwards

2021

Int. J. Prod. Manag. Eng.

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“…Organic farming requires maximum level of natural soil while inorganic farming can use minimum level natural soil and increase the growth of plants using chemicals. Suder and Kahraman (2018) solved the problem of organic and inorganic farming using TOPSIS method under fuzzy logic rankings. We have taken some part of data from Suder and Kahraman (2018) and edited the data to discuss uncertainty in farming decisions under rough environment.…”

Section: Organic and Inorganic Agricultural Food Investmentmentioning

confidence: 99%

“…Suder and Kahraman (2018) solved the problem of organic and inorganic farming using TOPSIS method under fuzzy logic rankings. We have taken some part of data from Suder and Kahraman (2018) and edited the data to discuss uncertainty in farming decisions under rough environment. The evaluations of the experts for each alternative under given criteria is given in Table 11 and the weights of experts for all criteria are given in Table 12.…”

Section: Organic and Inorganic Agricultural Food Investmentmentioning

confidence: 99%

Decision-making approaches based on color spectrum and D-TOPSIS method under rough environment

Sarwar¹

2020

Comp. Appl. Math.

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A rough set, an extension of a crisp set, is a mathematical tool to understand and model uncertainty without much prior information, additional adjustments or pre-defined membership functions. To manipulate the subjectivity and vagueness of decision-making problems, rough models provide more objective description of given information using upper and lower approximations. In this research paper, we study the absurdity and falsity of existing definition of rough graph. Based on rough relations, we introduce the concepts of rough graphs and rough digraphs and establish certain formulae, lower and upper bounds of color energy of rough graphs. Using D numbers, rough weights and rough entropy weights, we develop rough D-TOPSIS method which incorporates the capability to analyze uncertain and vague information without additional assumptions. We study the importance of rough information for the evaluation of water requirement in agricultural farming, investment analysis in organic and inorganic farming systems and illegal communication networks. Keywords Rough graph • Rough digraph • Spectrum • Color energy • Rough D-TOPSIS method • Agricultural farming investments • Illegal communication networks

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“…The TOPSIS method has a number of advantages (Selim et al, 2016;Suder & Kahraman, 2018;Xue et al, 2008;Zolfani et al, 2012):…”

Section: Introductionmentioning

confidence: 99%