scholarly journals Strategic Decision Selection Using Hesitant fuzzy TOPSIS and Interval Type-2 Fuzzy AHP: A case study

2014 ◽  
Vol 7 (5) ◽  
pp. 1002-1021 ◽  
Author(s):  
Sezi Cevik Onar ◽  
Başar Oztaysi ◽  
Cengiz Kahraman
Keyword(s):  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Strategic Decision ◽  
Interval Type

Evaluation of Renewable Energy Alternatives using Hesitant Fuzzy TOPSIS and Interval Type-2 Fuzzy AHP

2015 ◽  
pp. 191-224 ◽  
Author(s):  
Başar Öztayşi ◽  
Cengiz Kahraman
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Fuzzy Sets ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Priority Weights ◽  
Interval Type

The selection among renewable energy alternatives is a fuzzy multicriteria problem with many conflicting criteria under uncertainty. In many decision-making problems, the Decision Makers (DM) define their preference in linguistic form since it is relatively difficult to provide exact numerical values during the evaluation of alternatives. Therefore, in many studies, fuzzy logic is successfully used to model this kind of uncertainty. In this chapter, the authors try to capture this uncertainty by using interval type-2 fuzzy sets and hesitant fuzzy sets. They propose a fuzzy multicriteria method for the evaluation of renewable energy alternatives, in which the priority weights of the criteria are determined by interval type-2 fuzzy AHP, and the alternatives are ranked using hesitant fuzzy TOPSIS. A case study is also given.

Evaluation of Renewable Energy Alternatives Using Hesitant Fuzzy TOPSIS and Interval Type-2 Fuzzy AHP

2017 ◽  
pp. 1378-1412 ◽  
Author(s):  
Başar Öztayşi ◽  
Cengiz Kahraman
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Fuzzy Sets ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Priority Weights ◽  
Interval Type

The selection among renewable energy alternatives is a fuzzy multicriteria problem with many conflicting criteria under uncertainty. In many decision-making problems, the Decision Makers (DM) define their preference in linguistic form since it is relatively difficult to provide exact numerical values during the evaluation of alternatives. Therefore, in many studies, fuzzy logic is successfully used to model this kind of uncertainty. In this chapter, the authors try to capture this uncertainty by using interval type-2 fuzzy sets and hesitant fuzzy sets. They propose a fuzzy multicriteria method for the evaluation of renewable energy alternatives, in which the priority weights of the criteria are determined by interval type-2 fuzzy AHP, and the alternatives are ranked using hesitant fuzzy TOPSIS. A case study is also given.

scholarly journals An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 182 ◽  
Author(s):  
Melih Yucesan ◽  
Suleyman Mete ◽  
Faruk Serin ◽  
Erkan Celik ◽  
Muhammet Gul
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Fuzzy Topsis ◽  
Future Research ◽  
Negative Effects ◽  
Green Supplier Selection ◽  
Managerial Implications ◽  
Interval Type

Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today’s organizations are seeking new ways to reduce the negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.

Customer oriented intelligent DSS based on two-phased clustering and integrated interval type-2 fuzzy AHP and hesitant fuzzy TOPSIS

2020 ◽  
Vol 39 (5) ◽  
pp. 6121-6143
Author(s):  
Ozlem Senvar ◽  
Dilek Akburak ◽  
Necla Yel
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Ahp ◽  
Unit Cost ◽  
Fuzzy Topsis ◽  
Customer Experience ◽  
Customer Segmentation ◽  
Data Driven ◽  
Application Study ◽  
Interval Type

Firms need to integrate multiple business functions in order to acquire, analyze, model, and evaluate information necessary for better understanding customer behaviors and making data-driven decisions to enhance the customer experience journey. This study proposes a customer oriented intelligent decision support system (IDSS) to ultimately improve the customer experience journey. Besides, a real application study is handled for a multinational company located in Turkey, considering its abrasives product sales for years of 2017 and 2018. For the data utilized in application study, the proposed methodology is constructed for customer segmentation to develop appropriate data-driven marketing strategies for customers with similar values, preferences and other factors for creating customer-centric organizations. In this regard; firstly two-phased clustering process, which involves the hierarchical multivariate average linkage clustering algorithm and partitional k-means clustering algorithm, is used to present the number of clusters on the basis of three variables (expenditure, transaction and unit cost) and then to assign the customers to the related clusters (VIP, Platinum, Gold and Bronze), respectively. Secondly, the performances of company’s departments are ranked according to the preferences of customers from each segment considering 4Ps marketing mix concept via integrated methodology of interval type-2 Fuzzy AHP and hesitant fuzzy TOPSIS.

scholarly journals An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection

2018 ◽  
Author(s):  
Melih Yucesan ◽  
Suleyman Mete ◽  
Faruk Serin ◽  
Erkan Celik ◽  
Muhammet Gul
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Fuzzy Topsis ◽  
Future Research ◽  
Negative Effects ◽  
Green Supplier Selection ◽  
Managerial Implications ◽  
Interval Type

Supplier selection is one of the most important multi criteria decision making (MCDM) problems for decision makers in competitive market. Organizations of today’s world are seeking new ways to reduce negative effects of their organizations to the environment and to reach a greener system. At this point, green supplier selection concept has gained great importance with its ability on incorporating environmental or green criteria into the classical supplier selection practices. Therefore, in this study, it is aimed at proposing a multi-phase MCDM model based on Best-Worst Method (BWM) and interval type-2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS). A case study in a plastic injection molding facility in Turkey is performed to show the applicability of the proposed integrated methodology. The paper ensures insights into the decision making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.

The Use of Interval Type-2 Fuzzy TOPSIS in Supplier Selection

2014 ◽  
Vol 548-549 ◽  
pp. 1954-1958 ◽  
Author(s):  
Adawiyah Otheman ◽  
Ahmad Termimi Ab Ghani ◽  
Lazim Abdullah
Keyword(s):  
Supplier Selection ◽  
Feasible Solution ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Order Preference ◽  
Interval Type ◽  
Supplier Selection Problem ◽  
Selection Of

Supplier selection is one of the most important activities in supply chain management. However, the method of selecting appropriate supplier is not straightforward as it involves number of potential suppliers with diverse criteria. This paper aims to select the best supplier using interval type-2 fuzzy Technique for Order Preference by Similarity to Ideal Solution (IT2FTOPSIS) method. The method is used to a case study of computer components supplier selection. Of the four candidate suppliers, it is found that the supplier S3 is the best candidate. It indicates that the IT2FTOPSIS method offers a feasible solution to supplier selection problem.

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