Big data driven graphical information based fuzzy multi criteria decision making

Ullah, Aman; Noor-E-Alam, Muhammad

doi:10.1016/j.asoc.2017.11.026

Cited by 18 publications

(13 citation statements)

References 42 publications

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“…Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system. In the future, the application of the proposed Dice similarity measure of PFSs needs to be explored in decision making, risk analysis and many other fields under uncertain environment …”

Section: Discussionmentioning

confidence: 99%

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The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

2019

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The Pythagorean fuzzy set (PFS) is characterized by two functions expressing the degree of membership and the degree of nonmembership, which square sum of them is equal or less than 1. It was proposed as a generalization of a fuzzy set to deal with indeterminate and inconsistent information. In this study, we shall present some novel Dice similarity measures of PFSs and the generalized Dice similarity measures of PFSs and indicates that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures‐based multiple attribute group decision‐making models with Pythagorean fuzzy information. Then, we apply the generalized Dice similarity measures between PFSs to multiple attribute group decision making. Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system.

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Section: Discussionmentioning

confidence: 99%

“…In the future, the application of the proposed Dice similarity measure of PFSs needs to be explored in decision making, [73][74][75][76][77][78][79][80][81][82][83][84] risk analysis 85,86 and many other fields under uncertain environment. [87][88][89][90][91][92][93][94]…”

Section: Discussionmentioning

confidence: 99%

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

2019

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“…These historically stored data can be expressed in pieces of graphs (Domingo Galindo, 2016) because when visualized, these pieces of graphs can potentially present a large amount of information in an easy-to-understand way. Then, the necessary DRI in terms of pieces of crisp granular information can be extracted from these graphically (a) Data visualization for all suppliers (b) Extracting graph for a single supplier (c) Crisp granular information concerning cost (d) Crisp granular information concerning production capacity presented data according to (Ullah & Noor-E-Alam, 2018). Figure 8(a) shows the visualization of data collected for five suppliers concerning cost per unit of product and production capacity.…”

Section: Processing Time-series Datamentioning

confidence: 99%

“…After collecting all the crisp granular information for every alternative supplier, we fuzzify the frame of discernment associated with every non-time series attribute based on the fuzzification approach proposed in (Ullah & Noor-E-Alam, 2018). In the fuzzification process, the span of the frame of discernment is generated by the minimum and maximum of all the extracted crisp granular information for a certain attribute regarding all the alternative suppliers, while the number of linguistic terms is determined according to the granule definiteness axiom (A.M.M.…”

Section: Step 1(b): Transferring Non-time Series Based Graphical Datamentioning

confidence: 99%

“…Our proposed DSS overcomes this limitation by successfully converting and integrating the crisp granular information extracted from graphically presented data into triangular fuzzy based TOPSIS decision matrix. Crisp granular or c-granular information refers to the pieces of information entailing well-defined crisp or ill-defined perceptual boundary consisting of sharp numbers (Ullah & Noor-E-Alam, 2018;Lotfi A. Zadeh, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Resilient supplier selection in logistics 4.0 with heterogeneous information

Hasan

Jiang

Ullah

et al. 2020

Expert Systems with Applications

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Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment.The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Supplier's Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.

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A possibility distribution‐based multicriteria decision algorithm for resilient supplier selection problems

Jiang

Hasan

Faiz

et al. 2019

Multi Criteria Decision Anal

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Thus far, limited research has been performed on resilient supplier selection-a problem that requires simultaneous consideration of a set of numerical and linguistic evaluation criteria, which are substantially different from traditional supplier selection problem. Essentially, resilient supplier selection entails key sourcing decision for an organization to gain competitive advantage. In the presence of multiple conflicting evaluation criteria, contradicting decision makers, and imprecise decision relevant information (DRI), this problem becomes even more difficult to solve with the classical optimization approaches. Possibility distribution based Multi-Criteria Decision Analysis (MCDA) is a viable alternative approach for handling inherent uncertainty of imprecise DRI associated with the evaluation offered by a group of contradicting decision makers. However, prior research focusing on MCDA based supplier selection problem has been lacking in the ability to provide a seamless integration of numerical and linguistic evaluation criteria along with the consideration of multiple decision makers. To address these challenges, we present a comprehensive decision-making framework for ranking a set of suppliers from resiliency perspective. The proposed algorithm is capable of leveraging imprecise and aggregated DRI obtained from crisp numerical assessments and reliability adjusted linguistic appraisals from a group of decision makers. We adapt two popular tools -Single Valued Neutrosophic Sets (SVNS) and Interval-valued fuzzy sets (IVFS), and for the first time extend them to incorporate both crisp and linguistic evaluations in a group decision making platform to obtain aggregated SVNS and IVFS decision matrix. This information is then used to rank the resilient suppliers by using TOPSIS method. We present a case study to illustrate the mechanism of the proposed algorithm. A sensitivity analysis demonstrates the strength of the proposed algorithm to generate alternative ranking scheme with respect to the priorities of evaluation criteria, and thus shows the potential to provide a reliable decision-making framework.

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Big data driven graphical information based fuzzy multi criteria decision making

Cited by 18 publications

References 42 publications

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

Resilient supplier selection in logistics 4.0 with heterogeneous information

A possibility distribution‐based multicriteria decision algorithm for resilient supplier selection problems

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