Robust-fuzzy model for supplier selection under uncertainty: An application to the automobile industry

Rabieh, Masood; Modarres, Mohammad; Azar, Adel

doi:10.24200/sci.2017.4456

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…Next, AHP-DEMATEL was used to determine the inter-relationship among all criteria. Besides, Rabieh et al [30] developed a robust-fuzzy model for the purpose of supplier selection specifically under uncertainty. Finally, the developed model was applied in the automobile industry for a case study.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Intuitionistic Z-Numbers in Supplier Selection

Alam¹,

Khalif²,

Jaini³

2023

Intelligent Automation &Amp; Soft Computing

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Intuitionistic fuzzy numbers incorporate the membership and nonmembership degrees. In contrast, Z-numbers consist of restriction components, with the existence of a reliability component describing the degree of certainty for the restriction. The combination of intuitionistic fuzzy numbers and Z-numbers produce a new type of fuzzy numbers, namely intuitionistic Z-numbers (IZN). The strength of IZN is their capability of better handling the uncertainty compared to Zadeh's Z-numbers since both components of Z-numbers are characterized by the membership and non-membership functions, exhibiting the degree of the hesitancy of decision-makers. This paper presents the application of such numbers in fuzzy multi-criteria decision-making problems. A decision-making model is proposed using the trapezoidal intuitionistic fuzzy power ordered weighted average as the aggregation function and the ranking function to rank the alternatives. The proposed model is then implemented in a supplier selection problem. The obtained ranking is compared to the existing models based on Znumbers. The results show that the ranking order is slightly different from the existing models. Sensitivity analysis is performed to validate the obtained ranking. The sensitivity analysis result shows that the best supplier is obtained using the proposed model with 80% to 100% consistency despite the drastic change of criteria weights. Intuitionistic Z-numbers play a very important role in describing the uncertainty in the decision makers' opinions in solving decision-making problems.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Intuitionistic Z-Numbers in Supplier Selection

Alam¹,

Khalif²,

Jaini³

2023

Intelligent Automation &Amp; Soft Computing

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“…Suppliers' limited capacity and quality were taken into consideration. Rabieh et al [16] integrated robust optimization and fuzzy programming for supplier selection under multiple uncertainties. A real case from automobile industry has been provided.…”

Section: Supplier Selection and Allocation Problemmentioning

confidence: 99%

Two-stage game-theoretic approach to supplier evaluation, selection and order assignment

Wee

Jeng

et al. 2020

Scientia Iranica

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This study proposes a framework for supplier evaluation, selection, and assignment that incorporates a two-stage game-theoretic approach method. The objective is to provide insights to manufacturers in choosing suitable suppliers for different manufacturing processes. The framework applies to the decision logic of multiple manufacturing processes. In the first stage, a non-cooperative game model is utilized for supplier evaluation and selection. The interactive behaviors between a manufacturer and some supplier candidates are modeled and analyzed so that the supplier evaluation value (SEV) can be obtained using the Nash equilibrium. In the second stage, the supplier evaluation values become the input for the Shepley values calculation of each supplier under a cooperative game model. The Shapley values are utilized to create a set of limited supplier allocation. This paper provides managerial insights to verify the proposed approach on supplier selection and allocation. Thus, enables supply chain management (SCM) manager to optimize supplier evaluation, selection, and order assignment.

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“…Zadeh [1] introduced the fuzzy logic method o cially that provided more information in such a situation after quantum philosopher Black [2] and led data analysis to a new era. For example, Rabieha et al [3] applied the robust-fuzzy model to the automobile industry. In general, signature recognition, facial recognition, medical device like blood pressure monitor, plat number recognition via speed photography, or even debate in the court house are applications in reality where fuzzy theory can be applied.…”

Section: Introductionmentioning

confidence: 99%

What can fuzziness do for capability analysis based on fuzzy data

Chen

Hung

2019

Scientia Iranica

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One advantage of process incapability index over the classical process capability index is that it provides uncontaminated separation between process accuracy and process precision. However, the value of the index is di cult to determine accurately when process parameters cannot be determined precisely. In such a case, fuzzy set theory can be applied to obtain more exible and sensitive information. In this paper, a fuzzy process incapability index is proposed when the speci cation limits are assumed to be type-2 fuzzy. When the process mean and variance are unknown and when the observations are measured imprecisely, three fuzzy estimators are considered. A simulation study is conducted based on the Thin-Film-Transistor Liquid-Crystal Display (TFT-LCD) fuzzy process. By applying the total integral value method to the membership function of a fuzzy index, it becomes easier to make a comparison of the quality of di erent processes.

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Robust-fuzzy model for supplier selection under uncertainty: An application to the automobile industry

Cited by 6 publications

References 29 publications

Application of Intuitionistic Z-Numbers in Supplier Selection

Application of Intuitionistic Z-Numbers in Supplier Selection

Two-stage game-theoretic approach to supplier evaluation, selection and order assignment

What can fuzziness do for capability analysis based on fuzzy data

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