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A Probabilistic Uncertain Linguistic Decision-Making Model for Resilient Supplier Selection Based on Extended TOPSIS and BWM

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Abstract

Resilience is the sustainable competitive advantage of suppliers in the supply chain, and the ability of resilient suppliers to manage risk and perform better in supply than traditional suppliers in the event of disruption has driven the complexity of the current supply chain. Therefore, studying how to select a resilient supplier is necessary for establishing a supply chain with flexibility in the case of interruption. A hybrid fuzzy Multi-Criteria Group Decision-Making (MCGDM) framework is developed in this paper for Resilient Supplier Selection Problems (RSSPs). First, Probabilistic Uncertain Linguistic Term Sets (PULTSs) are introduced to deal with the subjectivity and uncertainty of experts’ assessments. Second, considering that experts may have different views on the relative importance of resilient criteria depending on their different knowledge backgrounds, the Probabilistic Uncertain Linguistic Best–Worst Method (PUL-BWM) is constructed to determine the weights of resilient criteria under different experts. In addition, given that the traditional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) cannot handle the information metrics with negative values or reflect the correlation of information, the extended TOPSIS method based on a novel Probabilistic Uncertain Linguistic Synthetic Correlation Coefficient (PULSCC) is constructed to select the optimal resilient supplier. The novel PULSCC also overcomes the drawbacks of the existing correlation coefficient between PULTSs by considering the mean, variance, and information completeness of PULTSs. Finally, an example of resilient supplier selection in the automotive industry is performed to validate the applicability and feasibility of the proposed approach. The sensitivity and comparative analyses are conducted to demonstrate the effectiveness and superiority of the proposed framework.

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Data Availability

The data used to support the findings of this study are included in the article.

Abbreviations

BWM:

Best–Worst Method

DEMATEL:

Decision-Making Trial And Evaluation Laboratory

IT2FS:

Interval Type-2 Fuzzy Set

LT:

Linguistic Term

MCDM:

Multi-Criteria Decision-Making

MCGDM:

Multi-Criteria Group Decision-Making

PLTS:

Probabilistic Linguistic Term Set

PULAV:

Probabilistic Uncertain Linguistic Average Value

PULE:

Probabilistic Uncertain Linguistic Element

PULNIS:

Probabilistic Uncertain Linguistic Negative Ideal Solution

PULPIS:

Probabilistic Uncertain Linguistic Positive Ideal Solution

PULSCC:

Probabilistic Uncertain Linguistic Synthetic Correlation Coefficient

PULTS:

Probabilistic Uncertain Linguistic Term Set

PULWA:

Probabilistic Uncertain Linguistic Weighted Averaging

PULWSCC:

Probabilistic Uncertain Linguistic Weighted Synthetic Correlation Coefficient

RSSP:

Resilient Supplier Selection Problem

TODIM:

An Acronym in Portuguese of Interactive and Multi-Criteria Decision-Making

TOPSIS:

Technique for Order of Preference by Similarity to Ideal Solution

ULT:

Uncertain Linguistic Term

VIKOR:

VIse Kriterijumska Optimizacija kompromisno Resenja

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Acknowledgements

This work is supported by the key project of National Natural Science Foundation of China (Grant No. U1904211); Training Program for Young Backbone Teachers in Higher Education Institutions of Henan Province (Grant No. 2021GGJS006); Support Program for Innovative Talents in Philosophy and Social Science of Henan Province (Grant No. 2023-CXRC-19); Precision Disciplines Support Program of Zhengzhou University (Grant No. XKLMJX202201); Outstanding Young Research Team in Social Sciences of Zhengzhou University (Grant No. 2023-QNTD-01).

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Authors and Affiliations

  1. School of Management, Zhengzhou University, Zhengzhou, 450001, China

    Jingjing Sun, Jichao Xu & Feng Zhu

  2. School of Business, Zhengzhou University, Zhengzhou, 450001, China

    Jingjing Sun, Yumin Liu & Ning Wang

  3. School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450015, China

    Yumin Liu

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  1. Jingjing Sun
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Correspondence to Ning Wang.

Appendix 1

Appendix 1

See Tables 11, 12, 13.

Table 11 The evaluation information of ex1
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Table 12 The evaluation information of ex2
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Table 13 The evaluation information of ex3
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Sun, J., Liu, Y., Xu, J. et al. A Probabilistic Uncertain Linguistic Decision-Making Model for Resilient Supplier Selection Based on Extended TOPSIS and BWM. Int. J. Fuzzy Syst. 26, 992–1015 (2024). https://doi.org/10.1007/s40815-023-01649-0

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