Application of Topsis Method to Supplier Selection in Iran Auto Supply Chain

Shahroudi, Kambiz; TONEKABONI, S.Maryam Shafaei

doi:10.20460/jgsm.2012615779

Cited by 20 publications

(16 citation statements)

References 5 publications

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“…To further illustrate the importance of considering reliability-based membership function in MCDA framework, we adopt the method proposed in (Shahroudi & Tonekaboni, 2012) to perform another set of comparative analysis. Since, the existing method do not consider multiple decision makers, we use the mean values to integrate the differential evaluation given by multiple decision makers to make parallel comparison.…”

Section: Figure 8 Comparison Between the Reliability-modified Model mentioning

confidence: 99%

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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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Section: Figure 8 Comparison Between the Reliability-modified Model mentioning

confidence: 99%

“…One of these studies (Shahroudi & Tonekaboni, 2012) proposed an application of TOPSIS in the supplier selection process in Iran Auto Supply Chain. In his research, both Numerical and Linguistic evaluation criteria are considered.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Hasan

Faiz

et al. 2019

Multi Criteria Decision Anal

View full text Add to dashboard Cite

show abstract

“…For selecting the best alternative from some available alternatives TOPSIS can be one of the excellent decision making method. The fundamental idea of the (TOPSIS) technique is that the best chosen alternative not just has the lowest distance from the optimum solution but also has the largest distance from the worst solution [7][8]. The TOPSIS technique was first presented by Hwang and Yoon in 1981, with the fundamental thought originating from the compromise idea of the alternative solution selected had the nearest distance to optimum solution and having the farthest distance from the worst solution [9].…”

Section: Introductionmentioning

confidence: 99%

Application of TOPSIS Method for the Optimization of Physical and Mechanical Characteristics of Graphite and Alumina Reinforced ZA - 27 Alloy Hybrid Composites

Kumar¹

2019

IJRASET

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The Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method utilizes to choose the best one from several attributes, combinations and objectives. In this paper calculation steps of TOPSIS method and selection of the best combination of weight % of hybrid reinforcement, the optimization of Gr and Al2O3 reinforced ZA-27 alloy hybrid composite on the basis of its physical and mechanical characteristics has been presented.

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“…It combines quantitative attributions (such as price, time, distance, etc.) and qualitative attributions (such as quality of relationship, quality assurance, reliability) and compares all alternatives together based on these attributions [5].…”

Section: Introductionmentioning

confidence: 99%

Decision Support Systems for Performance and Evaluation of Teachers in General-English Course by Using the SMARTER and TOPSIS Methods

Rasim

Rahman

Dewi

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. This research aims to implement Decision Support System (DSS) for choosing General English program teacher using Smarter and Topsis methods. The SMARTER method is used for determining weight of each criterion, where the TOPSIS method is used for calculate teacher ranks. The data used consist of some criteria and lecturer scores for each parameter. Both data are obtained from UPI Language Center Customer Satisfaction Report. Priority of criteria was decided based on questionnaire response from General English Program students. On accuracy aspect, the research shows that the combination of SMARTER and TOPSIS methods has 76% error rate. This research also shows that SMARTER and TOPSIS method has 90% stability. Based on questionnaire, 4 from 5 Balai Bahasa UPI's management choose teacher's rank from SMARTER and TOPSIS methods rather than manual.

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Application of Topsis Method to Supplier Selection in Iran Auto Supply Chain

Abstract: ABSTRACT

Cited by 20 publications

References 5 publications

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

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

Application of TOPSIS Method for the Optimization of Physical and Mechanical Characteristics of Graphite and Alumina Reinforced ZA - 27 Alloy Hybrid Composites

Decision Support Systems for Performance and Evaluation of Teachers in General-English Course by Using the SMARTER and TOPSIS Methods

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