A novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection problem

Pamučar, Dragan; Yazdani, Morteza; Obradović, Radojko; Kumar, Anil; Jiménez, Mercedes Torres

doi:10.1002/int.22279

Cited by 72 publications

(58 citation statements)

References 63 publications

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“…In this study, the real‐time criterion (0.098) had a high and significant contribution, as shown in Table 13. Based on that, the relative criteria weights computed using Equation (17) 143 produced 31 scenarios of criteria weight changing, where the elasticity coefficient (

α_{c}

) was used to compute the relative offset of all other criteria weights over the criterion of the higher significant contribution (real‐time criterion), as shown in Table 16.

w_{c} = ()(, 1 - w_{s}) \times ()(w_{c}^{o} / W_{c}^{0})) = w_{c}^{o} - normalΔ x α_{c},

where

w_{s}

is the higher significant contribution.

w_{c}^{o}

represents the original weight values computed using the IT2TR‐FWZIC method.

W_{c}^{0}

is the summation value of original weights for the changing criteria weight values.

normalΔ x

is the range of the changes applied to the weight values of the 12 smart key concepts (criteria), representing the limit values of the real‐time criterion, which were

- 0.0980 \leq normalΔ x \leq 0.9020

. …”

Section: Evaluation Processesmentioning

confidence: 99%

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Krishnan

Mohammed

Alnoor

et al. 2021

Int J of Intelligent Sys

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The benchmarking of smart e‐tourism data management applications falls under the problem of multicriteria decision‐making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision‐making framework on the basis of the extension of interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency (IT2TR‐FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e‐tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e‐tourism data management applications of each category and subcategory in smart e‐tourism. In the second phase, the integration of the IT2TR‐FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e‐tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real‐time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context‐awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e‐tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed.

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α_{c}

) was used to compute the relative offset of all other criteria weights over the criterion of the higher significant contribution (real‐time criterion), as shown in Table 16.

w_{c} = ()(, 1 - w_{s}) \times ()(w_{c}^{o} / W_{c}^{0})) = w_{c}^{o} - normalΔ x α_{c},

where

w_{s}

is the higher significant contribution.

w_{c}^{o}

represents the original weight values computed using the IT2TR‐FWZIC method.

W_{c}^{0}

is the summation value of original weights for the changing criteria weight values.

normalΔ x

is the range of the changes applied to the weight values of the 12 smart key concepts (criteria), representing the limit values of the real‐time criterion, which were

- 0.0980 \leq normalΔ x \leq 0.9020

. …”

Section: Evaluation Processesmentioning

confidence: 99%

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Krishnan

Mohammed

Alnoor

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…Step 5: Evaluation and selection of candidates that meet additional criterion s C . The evaluation is conducted by applying expression (3) (Pamucar et al, 2020b(Pamucar et al, , 2020c). If we assume that we chose as the dominant criterion, the final grade of the candidate is defined by applying the following expression: If the number of candidates who meet the dominant criterion is higher than the number of needed candidates, then the first p of candidates ranked by…”

Section: Multiple-criteria Evaluation Model For Medical Professionals Assigned To Covid Hospitalsmentioning

confidence: 99%

Multiple-criteria Evaluation Model for Medical Professionals Assigned to Temporary SARS-CoV-2 Hospitals

Žižović

Pamučar

Miljković

et al. 2021

Decis. Mak. Appl. Manag. Eng.

Self Cite

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Hospitals around the world, as health institutions with a key role in the health system, face problems while providing health services to patients with various types of diseases. Currently, those problems are intensified due to the pandemic caused by SARS-CoV-2 virus. This pandemic has caused an extreme spread of the disease with constantly changing needs of patients which impacts the capacities and overall functioning of hospitals. In order to meet the challenge of the COVID-19 (COronaVIrus Disease- 2019) pandemic, health systems must adjust to new circumstances and establish separate hospitals exclusive for patients infected with SARS-CoV-2 virus. In the process of creating COVID-19 hospitals, health systems face a shortage of medical professionals trained for work in COVID-19 hospitals. Using this as a starting point, this study puts forward a two-phase model for the evaluation and selection of nurses for COVID-19 hospitals. Each phase of the model features a separate multiple-criteria model. In the first phase, a multiple-criteria model with a dominant criterion is formed and candidates who meet the defined requirements are evaluated. In the second phase, a modified multiple-criteria model is formed and used to evaluate medical professionals who do not meet the requirements of the dominant criterion. By applying this model, two groups of medical professionals are defined: 1) medical professionals who completely meet the requirements for working in COVID-19 hospitals and 2) medical professionals who require additional training. The criteria for evaluation of medical professionals in this multiple-criteria model are defined based on research conducted on medical professionals assigned to the COVID-19 Crisis Response Team during the COVID-19 pandemic in the Republic of Serbia. The model was tested on a real example of evaluating medical professionals assigned to the COVID-19 hospital in Sombor. The model for evaluating medical professionals presented in this paper can help decision makers in hospitals and national policy makers to determine the readiness level of hospitals for working in the conditions of the COVID-19 pandemic, as well as underline the areas in which hospitals are not ready to meet the challenges of the pandemic.

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“…To date, plenty of techniques have been widely developed in neutrosophic environments to solve problems in different fields. Recently, Long et al (2019) extended the association matrix into neutrosophic environments for addressing fuzzy clustering issues; Pamucar et al (2020) presented a hybrid decision method to select suitable suppliers under neutrosophic circumstances; and Abdel‐Basset et al (2020) scheduled the time in projects and assessed the linear time–cost tradeoffs with neutrosophic numbers. However, few pricing decisions have been made within a neutrosophic environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Neutrosophic game pricing methods with risk aversion for pricing of data products

Luo

Xing

2021

Expert Systems

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With the progressive development of satellite image data products, their pricing strategies become more and more important for enterprises to earn profits. The objective of this study is to explore several game pricing methods with risk aversion for pricing of data products in neutrosophic environments. First, to reflect the uncertainty of problem parameters, the idea of neutrosophic variables is adopted. With the aid of neutrosophic variables, the truth, indeterminacy and falsity degrees of players can be intuitively and conveniently obtained. Subsequently, considering the risk aver-

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A novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection problem

Cited by 72 publications

References 63 publications

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Multiple-criteria Evaluation Model for Medical Professionals Assigned to Temporary SARS-CoV-2 Hospitals

Neutrosophic game pricing methods with risk aversion for pricing of data products

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