A NOVEL HYBRID SWARA AND VIKOR METHODOLOGY FOR SUPPLIER SELECTION IN AN AGILE ENVIRONMENT

2013 ◽  
Vol 19 (3) ◽  
pp. 533-548 ◽  
Author(s):  
Maryam Alimardani ◽  
Sarfaraz Hashemkhani Zolfani ◽  
Mohammad Hasan Aghdaie ◽  
Jolanta Tamošaitienė
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supplier Selection ◽  
Selection Process ◽  
Multiple Attribute Decision Making ◽  
Second Phase ◽  
Ratio Analysis ◽  
Business Environments ◽  
Agile Supply Chain ◽  
Multiple Attribute

The concept of the agile supply chain has been taken into account as means of achieving a high competitive edge in rapidly changing business environments. Supply partner selection is one of the most appealing issues for agile supply chain management, which have recently been studied by academicians and practitioners. Due to a large number of factors to be considered, supplier selection process is a difficult task for every company. Therefore, supplier selection process can be viewed as a multiple attribute decision-making (MADM) problem. In this paper, a novel hybrid MADM method is proposed for agile supplier selection based on four criteria including performance, cost, flexibility and technology. Two MADM methods, including step-wise weight assessment ratio analysis (SWARA) and Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are applied in decision-making process. More precisely, SWARA is used for determining the importance of each criterion and calculating their weights and VIKOR is applied for evaluating alternatives as well as ranking supplier alternatives from the best to the worst. More precisely, the first phase of the proposed methodology, step-wise weight assessment ratio analysis (SWARA), is useful for determining the importance of each criterion and calculating the weight of each criterion, and the second phase with Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is useful for evaluating alternatives as well as ranking supplier alternatives from the best to the worst. Finally, a real case-study is presented to demonstrate the applicability of the proposed methodology. As a result, the model can help managers to evaluate and select the best supplier regarding own company strategies, resources, policies and etc. for their organization.

scholarly journals Some generalized Pythagorean 2-tuple linguistic Bonferroni mean operators in multiple attribute decision making

2018 ◽  
Vol 12 (4) ◽  
pp. 387-398 ◽  
Author(s):  
Xiyue Tang ◽  
Guiwu Wei
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supplier Selection ◽  
Multiple Attribute Decision Making ◽  
Green Supply Chain ◽  
Supply Chain Strategy ◽  
Bonferroni Mean ◽  
Multiple Attribute ◽  
Environmental Consideration ◽  
Selection Methodology

Green supply chain management is a strategy which strengthens and integrates environmental consideration into whole supply chain. The green strategic supplier plays an important role in the implementation of green supply chain strategy. In the selection methodology of a strategic green supplier, some special requirements are needed which are different from the traditional supplier selection practices. In this paper, we combine the generalized weighted BM operator with Pythagorean 2-tuple linguistic numbers to propose the generalized Pythagorean 2-tuple linguistic-weighted Bonferroni mean operator, and then the multiple attribute decision-making methods are developed based on this operator. Finally, we use an example for green supplier selection to illustrate the multiple attribute decision-making process of the proposed methods.

scholarly journals Models for MADM with Single-Valued Neutrosophic 2-Tuple Linguistic Muirhead Mean Operators

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 442 ◽  
Author(s):  
Jie Wang ◽  
Jianping Lu ◽  
Guiwu Wei ◽  
Rui Lin ◽  
Cun Wei
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Multiple Attribute Decision Making ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Chain Management ◽  
Green Supplier Selection ◽  
Multiple Attribute

In this article, we expand the Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator with single-valued neutrosophic 2-tuple linguistic numbers (SVN2TLNs) to propose the single-valued neutrosophic 2-tuple linguistic Muirhead mean (SVN2TLMM) operator, the single-valued neutrosophic 2-tuple linguistic weighted Muirhead mean (SVN2TLWMM) operator, the single-valued neutrosophic 2-tuple linguistic dual Muirhead mean (SVN2TLDMM) operator, and the single-valued neutrosophic 2-tuple linguistic weighted dual Muirhead mean (SVN2TLWDMM) operator. Multiple attribute decision making (MADM) methods are then proposed using these operators. Finally, we utilize an applicable example for green supplier selection in green supply chain management to prove the proposed methods.

scholarly journals Dual hesitant pythagorean fuzzy Hamacher aggregation operators in multiple attribute decision making

2017 ◽  
Vol 27 (3) ◽  
pp. 365-395 ◽  
Author(s):  
Guiwu Wei ◽  
Mao Lu
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Fuzzy Information ◽  
Chain Management ◽  
Multiple Attribute ◽  
The Ideal

AbstractIn this paper, we investigate the multiple attribute decision making (MADM) problem based on the Hamacher aggregation operators with dual Pythagorean hesitant fuzzy information. Then, motivated by the ideal of Hamacher operation, we have developed some Hamacher aggregation operators for aggregating dual hesitant Pythagorean fuzzy information. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the dual hesitant Pythagorean fuzzy multiple attribute decision making problems. Finally, a practical example for supplier selection in supply chain management is given to verify the developed approach and to demonstrate its practicality and effectiveness.

An integrated approach for cutting fluid selection using multiple attribute decision making methods

2021 ◽  
Vol 15 (1) ◽  
pp. 7860-7873
Author(s):  
S. Dhanalakshmi ◽  
T. Rameshbabu
Keyword(s):  
Decision Making ◽  
Aluminium Alloy ◽  
Selection Process ◽  
Integrated Approach ◽  
Multiple Attribute Decision Making ◽  
Machining Process ◽  
Cutting Fluid ◽  
Maximum Benefit ◽  
Multiple Attribute ◽  
Cutting Fluid Selection

The choice of most appropriate cutting liquid in any machining process should be performed to attain maximum benefit. Selection of cutting fluid needs more than one dimension. For this purpose, many criteria should be deemed in the selection process.  In this study decision making methods such as AHP, TOPSIS and VIKOR are employed to select the suitable cutting fluid. AHP is used to compute the objective weights for the  criteria.The three alternatives considered are Ahonol- 7, Blaser and YBI. A case study of cutting fluid selection for machining LM 25 Aluminium alloy which finds application in automobile industries is presented to explain the applicability and suitability of the anticipated method. Ranking of alternatives in the above methods suggests Ahonol – 7 as the best cutting fluid for CNC turning LM 25 aluminium alloy.

scholarly journals Models for Multiple Attribute Decision Making with Some 2-Tuple Linguistic Pythagorean Fuzzy Hamy Mean Operators

Mathematics ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 236 ◽  
Author(s):  
Xiumei Deng ◽  
Jie Wang ◽  
Guiwu Wei ◽  
Mao Lu
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Fuzzy Numbers ◽  
Special Kind ◽  
Multiple Attribute Decision Making ◽  
Linguistic Terms ◽  
Multiple Integration ◽  
Multiple Attribute ◽  
Pythagorean Fuzzy Numbers ◽  
Actual Decision

The Hamy mean (HM) operator, as a useful aggregation tool, can capture the correlation between multiple integration parameters, and the 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs) are a special kind of Pythagorean fuzzy numbers (PFNs), which can easily describe the fuzziness in actual decision making by 2-tuple linguistic terms (2TLTs). In this paper, to consider both Hamy mean (HM) operator and 2TLPFNs, we combine the HM operator, weighted HM (WHM) operator, dual HM (DHM) operator, and dual WHM (DWHM) operator with 2TLPFNs to propose the 2-tuple linguistic Pythagorean fuzzy HM (2TLPFHM) operator, 2-tuple linguistic Pythagorean fuzzy WHM (2TLPFWHM) operator, 2-tuple linguistic Pythagorean fuzzy DHM (2TLPFDHM) operator and 2-tuple linguistic Pythagorean fuzzy DWHM (2TLPFDWHM) operator. Then some multiple attribute decision making (MADM) procedures are developed based on these operators. At last, an applicable example for green supplier selection is given.

scholarly journals Sistem Pendukung Keputusan Pemberian Reward Pegawai Menggunakan Metode TOPSIS

2020 ◽  
Vol 18 (1) ◽  
pp. 11
Author(s):  
Aisyah Mutia Dawis
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Selection Process ◽  
Multiple Attribute Decision Making ◽  
Management Problem ◽  
Multiple Attribute ◽  
Ideal Solutions ◽  
Order Preference ◽  
Topsis Technique ◽  
A Company

Every company has management providing wages or rewards to employees. This is because employees are one of the resources that are used as a driving force in advancing a company. Besides, many companies provide rewards to their employees with the aim of motivating employees to help more. There is management problem in PKU Muhammadiyah Gamping Hospital for determining the number of rewards obtained by employees because many variables are determined. Therefore, the need of management information system can facilitate the Management of the PKU Muhammadiyah Gamping Hospital in determining decision making for providing rewards. One method that is often used in implementing decision support systems is Multiple Attribute Decision Making (MADM), focusing TOPSIS (Technique for Order Preference with Similarities to Ideal Solutions). By the implementation of the decision support system, PKU Muhammadiyah Gamping Hospital can carry out the selection process more efficiently.The test results by matching the employee data results at PKU Muhammadiyah Hospital obtained 95.83% accuracy so that this system can help the PKU Muhammadiyah Hospital in determining employee rewards.

scholarly journals A Method Based on Intuitionistic Fuzzy Dependent Aggregation Operators for Supplier Selection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Fen Wang ◽  
Shouzhen Zeng ◽  
Chonghui Zhang
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Intuitionistic Fuzzy ◽  
Strategic Factor ◽  
Multiple Attribute ◽  
Fuzzy Aggregation ◽  
Decision Making Problem

Recently, resolving the decision making problem of evaluation and ranking the potential suppliers have become as a key strategic factor for business firms. In this paper, two new intuitionistic fuzzy aggregation operators are developed: dependent intuitionistic fuzzy ordered weighed averaging (DIFOWA) operator and dependent intuitionistic fuzzy hybrid weighed aggregation (DIFHWA) operator. Some of their main properties are studied. A method based on the DIFHWA operator for intuitionistic fuzzy multiple attribute decision making is presented. Finally, an illustrative example concerning supplier selection is given.

scholarly journals Multiple Attribute Decision Making (MADM) in Proportional-Stop-Loss Reinsurance

2017 ◽  
Author(s):  
Jiexuan Wang
Keyword(s):  
Decision Making ◽  
Selection Process ◽  
Multiple Attribute Decision Making ◽  
Simulation Software ◽  
Insurance Companies ◽  
Insurance Company ◽  
Multiple Attribute ◽  
Study Results ◽  
Stop Loss

This article addresses reinsurance decision making process, which involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. In contrast to existing literature on pure proportional reinsurance or stop-loss reinsurance, this article focuses on the combination into Proportional-Stop-loss reinsurance design which better addresses interest of both parties. In terms of methodology, the significant contribution of the study is to incorporate Multiple Attribute Decision Making (MADM) into modelling the reinsurance selection. The Multi-Objective Decision Making (MODM) model is applied in designing reinsurance alternatives. Then MADM is applied to aid insurance companies in choosing the most appropriate reinsurance contract. To illustrate the feasibility of incorporating intelligent decision supporting system in reinsurance market, the study includes a numerical case study using simulation software @Risk in modeling insurance claims, and programming in MATLAB to realize MADM. Managerial implications could be drawn from the case study results. More specifically, when choosing the most appropriate reinsurance, insurance companies should base their decision on multiple measurements instead of single-criteria decision making models for their decisions to be more robust.

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