The interval TOPSIS method for group decision making

Cel – Celem pracy jest przedstawienie nowego podejścia do rankingu wariantów decyzyjnych z danymi przedziałowymi dla grupowego podejmowania decyzji, wykorzystującego metodę TOPSIS. Metodologia badania – W proponowanym podejściu, wszystkie pojedyncze oceny decydentów są brane pod uwagę w wyznaczaniu końcowych ocen wariantów decyzyjnych oraz ich rankingu. Kluczowym jego elementem jest przekształcenie macierzy decyzyjnych dostarczonych przez decydentów, w macierze wariantów decyzyjnych. Wynik – Nowe podejście do grupowego podejmowania decyzji wykorzystujące metodę TOPSIS. Oryginalność/wartość – Proponowane podejście jest nowatorskie oraz łatwe w użyciu.Goal – The purpose of the paper is to present a new approach to the ranking of alternatives with interval data for group decision making using the TOPSIS method. Research methodology – In the proposed approach, all individual assessments of decision makers are taken into account in determining the final assessments of alternatives and their ranking. The key stage of the proposed approach is the transformation of the decision matrices provided by the decision makers into a matrices of alternatives. Score – A new approach for group decision making using the TOPSIS method. Originality/value – The proposed approach is innovative and easy to use.Badania zostały zrealizowane w ramach pracy nr S/WI/1/2016 i sfinansowane ze środków na naukę [email protected]ł Informatyki, Politechnika BiałostockaAbdullah L., Adawiyah C.W.R., 2014, Simple Additive Weighting Methods of Multicriteria Decision Making and Applications: A Decade Review, “International Journal of Information Processing and Management”, vol. 5(1), pp. 39-49.Behzadian M., Otaghsara S.K., Yazdani M., Ignatius J., 2012, A state-of the art survey of TOPSIS applications, “Expert Systems with Applications”, vol. 39, pp. 13051-13069.Boran F.E., Genc S., Kurt M., Akay D., 2009, A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, “Expert Systems with Applications”, vol. 36, pp. 11363-11368.Chen C.T., 2000, Extensions of the TOPSIS for group decision-making under fuzzy environment, “Fuzzy Sets and Systems”, vol. 114, pp. 1-9.Cloud M. J., Kearfott R.B., Moore R.E., 2009, Introduction to Interval Analysis, SIAM, Philadelphia.Dymova L., Sevastjanova P., Tikhonenko A., 2013, A direct interval extension of TOPSIS method, “Expert Systems with Applications”, vol. 40, pp. 4841-4847.Hu B.Q., Wang S., 2006, A Novel Approach in Uncertain Programming Part I: New Arithmetic and Order Relation for Interval Numbers, “Journal of Industrial and Management Optimization”, vol. 2(4), pp. 351-371.Hwang C.L., Yoon K. 1981 Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Berlin.Jahanshahloo G.R., Hosseinzadeh Lotfi F., Izadikhah M., 2006, An Algorithmic Method to Extend TOPSIS for Decision Making Problems with Interval Data, “Applied Mathematics and Computation”, vol. 175, pp. 1375-1384.Kacprzak D., 2017, Objective Weights Based on Ordered Fuzzy Numbers for Fuzzy Multiple Criteria Decision Making Methods, “Entropy”, vol. 19(7), pp. 373.Kacprzak D., 2018, Metoda SAW z przedziałowymi danymi i wagami uzyskanymi za pomocą przedziałowej entropii Shannona, „Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach”, vol. 348, pp. 144-155.Kacprzak D., 2019, A doubly extended TOPSIS method for group decision making based on ordered fuzzy numbers, “Expert Systems with Applications”, vol. 116, pp. 243-254.Roszkowska E., 2009, Application TOPSIS methods for ordering offers in buyer-seller transaction, “OPTIMUM, Studia Ekonomiczne”, vol. 3(43), pp. 117-133.Roszkowska E., 2011, Multi-Criteria Decision Making Models by Applying the TOPSIS Method to Crisp and Interval Data, “Multiple Criteria Decision Making”, vol. 6, pp. 200-230.Roszkowska E., Kacprzak D., 2016, The fuzzy SAW and fuzzy TOPSIS procedures based on ordered fuzzy numbers, “Information Sciences”, vol. 369, pp. 564-584.Rudnik K., Kacprzak D., 2017, Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system, “Applied Soft Computing”, vol. 52, pp. 1020-1041.Senvar O., Otay İ., Boltürk E., 2016, Hospital site selection via hesitant fuzzy TOPSIS. “IFAC-PapersOnLine”, vol. 49, pp. 1140-1145.Shih H.S., Shyur H.J., Lee E.S., 2007, An extension of TOPSIS for group decision making, “Mathematical and Computer Modelling”, vol. 45, pp. 801-813.Wang T.C., Chang T.H., 2007, Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment, “Expert Systems with Applications”, vol. 33, pp. 870-880.Ye F., Li Y.N., 2009, Group multi-attribute decision model to partner selection in the formation of virtual enterprise under incomplete information, “Expert Systems with Applications”, vol. 36, pp. 9350-9357.Yue Z., 2011, An extended TOPSIS for determining weights of decision makers with interval numbers, “Knowledge-Based Systems”, vol. 24, pp. 146-153.Yue Z., 2012, Developing a straightforward approach for group decision making based on determining weights of decision makers, “Applied Mathematical Modelling”, vol. 36, pp. 4106-4117.4(94)25627

Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

Evaluation of e-learning web sites using fuzzy axiomatic design based approach

High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered

Multi-criteria decision making with linguistic labels: a comparison of two methodologies applied to energy planning

This paper compares two multi-criteria decision making (MCDM) approaches based on linguistic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology introduced by Kaya and Kahraman in 2011. The second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assessment and selection of the most suitable types of energy in a geographical area.Peer ReviewedPostprint (published version

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version

A Multi-Criteria Neutrosophic Group Decision Making Method Based TOPSIS for Supplier Selection

The process of multi-criteria group decision making (MCGDM) is of determining the best choice among all of the probable alternatives. The problem of supplier selection on which decision maker has usually vague and imprecise knowledge is a typical example ofmulti criteria group decision-making problem. The conventional crisp techniques has notmuch effective for solvingMCDMproblems because of imprecise or fuzziness nature of the linguistic assessments. To find the exact values for MCGDM problems is both difficult and impossible in more cases in real world. So, it is more reasonable to consider the values of alternatives according to the criteria as single valued neutrosophic sets (SVNS). This paper deal with the technique for order preference by similarity to ideal solution (TOPSIS) approach and extend the TOPSIS method to MCGDM problem with single valued neutrosophic information. The value of each alternative and the weight of each criterion are characterized by single valued neutrosophic numbers. Here, the importance of criteria and alternatives is identified by aggregating individual opinions of decision makers (DMs) via single valued neutrosophic weighted averaging (SVNWA) operator. The proposed method is, easy use, precise and practical for solving MCGDM problem with single valued neutrosophic data. Finally, to show the applicability of the developed method, a numerical experiment for supplier choice is given as an application of single valued neutrosophic TOPSIS method at end of this paper

Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

As enterprises pay more and more attention to environmental issues, the green supply chain management (GSCM) mode has been extensively utilized to guarantee profit and sustainable development. Greensupplierselection(GSS),whichisakeysegmentofGSCM,hasbeeninvestigated to put forward plenty of GSS approaches