Hiyerarşik gri ilişkisel analiz yöntemiyle çok kriterli çağri merkezi yeri seçimi

2017

PRR

Purpose This paper aims to apply an integrated data envelopment analysis (DEA) and analytic hierarchy process (AHP) approach to a multi-hierarchy grey relational analysis (GRA) model. Consistent with the most real-life applications, the authors focus on a two-level hierarchy in which the attributes of similar characteristics can be grouped into categories. Nevertheless, the proposed approach can be easily extended to a three-level hierarchy in which attributes might also belong to different sub-categories and further be linked to categories. Design/methodology/approach The procedure of incorporating the DEA and AHP methods in a two-level GRA may be broken down into a series of steps. The first three steps are under the heading of attributes and the latter three steps are under the heading of categories as follows: computing the grey relational coefficients of attributes for each alternative using the basic GRA model which further provides the required (output) data for an additive DEA model; computing the priority weights of attributes and categories using the AHP method which provides a priori information on the adjustments of attributes and categories in additive DEA models; computing the grey relational grades of attributes in each category for alternatives using an additive DEA model; converting the grey relational grades of attributes to the grey relational coefficients of categories; computing the grey relational grades of categories for alternatives using an additive DEA model; computing the dissimilarity grades of categories for the tied alternatives using an additive DEA exclusion model. Findings The proposed approach provides a more reasonable and encompassing measure of performance in a hierarchy GRA, based on which the overall ranking position of alternatives is obtained. A case study of a wastewater treatment technology selection verifies the effectiveness of this approach. Originality/value This research is a step forward to overcome the current shortcomings in a hierarchy GRA by extracting the benefits from both the objective and subjective weighting methods.

Section: Introductionmentioning

confidence: 99%

Hierarchy grey relational analysis using DEA and AHP

2017

PRR

“…If the selection of experts is different, then the weights obtained will be different (Liu, 2009;Liu and Chen, 2004). The application of AHP with GRA can be seen in the studies by Birgün and Güngör (2014), Jia et al (2011) and Zeng et al (2007).…”

Section: Introductionmentioning

confidence: 99%

An integrated approach to grey relational analysis, analytic hierarchy process and data envelopment analysis

2016

JCC

Purpose -This paper aims to propose an integration of the analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods in a multiattribute grey relational analysis (GRA) methodology in which the attribute weights are completely unknown and the attribute values take the form of fuzzy numbers. Design/methodology/approach -This research has been organized to proceed along the following steps: computing the grey relational coefficients for alternatives with respect to each attribute using a fuzzy GRA methodology. Grey relational coefficients provide the required (output) data for additive DEA models; computing the priority weights of attributes using the AHP method to impose weight bounds on attribute weights in additive DEA models; computing grey relational grades using a pair of additive DEA models to assess the performance of each alternative from the optimistic and pessimistic perspectives; and combining the optimistic and pessimistic grey relational grades using a compromise grade to assess the overall performance of each alternative. Findings -The proposed approach provides a more reasonable and encompassing measure of performance, based on which the overall ranking position of alternatives is obtained. An illustrated example of a nuclear waste dump site selection is used to highlight the usefulness of the proposed approach. Originality/value -This research is a step forward to overcome the current shortcomings in the weighting schemes of attributes in a fuzzy multiattribute GRA methodology.

“…AHP usually involves three basic functions: structuring complexities, measuring on a ratio scale, and synthesizing [20]. The application of AHP with GRA can be seen in [2,9,24].…”

Section: Introductionmentioning

confidence: 99%

Multiple attribute grey relational analysis using DEA and AHP

2016

Complex Intell. Syst.

This research proposes an integrated data envelopment analysis (DEA) and analytic hierarchy process (AHP) approach to obtain attribute weights in a grey relational analysis (GRA) method. First, this can be implemented by developing a DEA-based GRA model to obtain attribute weights for the alternative under assessment. Second, weight bounds, using AHP, can be incorporated in the DEA-based GRA model to reflect the priority weights of attributes. Third, the effects of incorporating weight bounds on attribute weights can be analyzed by developing a parametric distance model. Increasing the value of a parameter in a domain of grey relational loss, i.e., a reduction in grey relational grade, we explore the tradeoff relationship between the grey relational grade and the priority weights of attributes for each alternative. This may result in various ranking positions for each alternative in comparison with the other alternatives. An illustrated example of selecting dispatching rules is also presented to highlight the usefulness of the proposed approach.