A suitable site selection for sustainable bioenergy production facility by using hybrid multi-criteria decision making approach, case study: Turkey

Emeksiz, Cem; YÜKSEL, Abdullah

doi:10.1016/j.fuel.2022.123214

Cited by 14 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 2 The operator of the crisp matrix is shown in Equation (6). We can get the judgement matrix as is shown in Equation (28).…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Convert a crisp matrix to a judgement matrix. Just like Case 1, for instance, we can calculate the element R c13 = 0.66 of the crisp matrix Equation ( 18), according to Equation (6).…”

Section: Priority Weights Of Alternatives Based On D Matrixmentioning

confidence: 99%

“…Multi-attribute decision making is an important part of decision theory and modern decision science [1]. It has been widely used in investment decision making [2], project evaluation [3], scheme selection [4], factory site selection [5,6], comprehensive evaluation of economic benefits [7], etc. Therefore, it is of great practical and theoretical significance to study effective and practical multi-attribute decision making methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supplier Selection Model Based on D Numbers and Transformation Function

Xiong

Hong

2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

Selecting reasonable suppliers can effectively improve the efficiency of enterprise supply chain management. Among them, expert evaluation is an important part of supplier selection problem, but the uncertainty, fuzziness and incompleteness of expert opinions make supplier selection problem difficult to solve. In order to systematically and effectively solve the uncertainty, ambiguity and incompleteness in supplier selection problem, this paper presents a new supplier selection method based on D numbers and transformation function. First, fuzzy preference relation is generated based on the decision matrix of pairwise comparisons given by experts. D numbers which can effectively deal with uncertain information extend fuzzy preference relation (D matrix). Second, the D matrix is converted into a crisp matrix form based on the integration representation of D numbers according to different situations whether or not the information in D matrix is complete. Third, the crisp matrix is converted into judgement matrix by using the transformation functions. Finally, analytic hierarchy process (AHP) method is applied based on the judgment matrix to give a priority weights for decision making. Three numerical examples and application of the supplier selection are used to show the feasibility and effectiveness of the proposed method.

show abstract

“…Step 2 The operator of the crisp matrix is shown in Equation (6). We can get the judgement matrix as is shown in Equation (28).…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Convert a crisp matrix to a judgement matrix. Just like Case 1, for instance, we can calculate the element R c13 = 0.66 of the crisp matrix Equation ( 18), according to Equation (6).…”

Section: Priority Weights Of Alternatives Based On D Matrixmentioning

confidence: 99%