Multi-criteria group decision-making method for green supplier selection based on distributed interval variables

Addressing the multi-criteria group decision making problem with interval attribute values and attribute weights, this paper proposes a decision method based on attribute distribution information. The selection of green suppliers is taken as an example for decision analysis. First, in the case of group decision-making, the quantitative values of the evaluation attributes of green suppliers are imputed by decision-makers, and the relevant distributions are constructed for each attribute. Next, combined with the ranges of attribute values, the random interval values are used to describe the information represented by each attribute to overcome the loss caused by the aggregation of individual expert information into group information. We then propose the distributed interval weighted arithmetic average (DIWAA) operator and corresponding operation rules, which realizes the fusion of qualitative data and quantitative judgment. Thus, the proposed approach allows ensuring reasonable results of the multi-criteria analysis. We also construct a ranking method for alternatives based on distributed interval comprehensive scores. Finally, we verify the feasibility and effectiveness of the proposed method for the task of green supplier selection through numerical experiments

Method of Group Decision Making with Interval Grey Numbers based on Grey Correlation and Relative Close Degree

With the development of society, big data\u27s concept is mentioned more and more. However, in the numerous and complex mass data, the effective information is often very limited, and the data often show the complex characteristics of "close and orderly short-term correlation and large long-term disturbance", which is difficult to be expressed by a long sequence composed of a single real value. This paper takes the interval grey number as the research object for the uncertain system of "less data and poor information". According to the multi-attribute group decisions problem with attribute’s values of interval grey number and the weights of decision-makers and attributes that are completely unknown, a method of group decision making with interval grey numbers based on grey correlation and relative close degree is proposed. For the purpose of making effective decisions, the weights of decision-makers and attributes are calculated by correlation degree of decision information which is measured by the model of grey correlation analysis based on interval grey number deviation degree. On this basis, the correlation degree between the scheme and the positive and negative ideal schemes are computed, and then the grey correlation and relative close degree of the scheme is calculated. Finally, the scheme is sorted according to the value of the grey correlation and relative close degree. The rationality and feasibility of the proposed method are verified by numerical examples

TOPSIS-RTCID for range target-based criteria and interval data

[EN] The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is receiving considerable attention as an essential decision analysis technique and becoming a leading method. This paper describes a new version of TOPSIS with interval data and capability to deal with all types of criteria. An improved structure of the TOPSIS is presented to deal with high uncertainty in engineering and engineering decision-making. The proposed Range Target-based Criteria and Interval Data model of TOPSIS (TOPSIS-RTCID) achieves the core contribution in decision making theories through a distinct normalization formula for cost and benefits criteria in scale of point and range target-based values. It is important to notice a very interesting property of the proposed normalization formula being opposite to the usual one. This property can explain why the rank reversal problem is limited. The applicability of the proposed TOPSIS-RTCID method is examined with several empirical litreture’s examples with comparisons, sensitivity analysis, and simulation. The authors have developed a new tool with more efficient, reliable and robust outcomes compared to that from other available tools. The complexity of an engineering design decision problem can be resolved through the development of a well-structured decision making method with multiple attributes. Various decision approches developed for engineering design have neglected elements that should have been taken into account. Through this study, engineering design problems can be resolved with greater reliability and confidence.Jahan, A.; Yazdani, M.; Edwards, K. (2021). TOPSIS-RTCID for range target-based criteria and interval data. International Journal of Production Management and Engineering. 9(1):1-14. https://doi.org/10.4995/ijpme.2021.13323OJS11491Ahn, B.S. (2017). 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Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications. Materials & Design, 87, 949-959. https://doi.org/10.1016/j.matdes.2015.08.087Hafezalkotob, A., Hafezalkotob, A. (2016). Interval MULTIMOORA method with target values of attributes based on interval distance and preference degree: biomaterials selection. Journal of Industrial Engineering International, 13, 181-198. https://doi.org/10.1007/s40092-016-0176-4Hafezalkotob, A., Hafezalkotob, A. (2017). Interval target-based VIKOR method supported on interval distance and preference degree for machine selection. Engineering Applications of Artificial Intelligence, 57, 184-196. https://doi.org/10.1016/j.engappai.2016.10.018Hafezalkotob, A., Hafezalkotob, A., Sayadi, M.K. (2016). Extension of MULTIMOORA method with interval numbers: An application in materials selection. 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Developing WASPAS-RTB method for range target-based criteria: toward selection for robust design. Technological and Economic Development of Economy, 24, 1362-1387. https://doi.org/10.3846/20294913.2017.1295288Jahan, A., Bahraminasab, M., Edwards, K.L. (2012). A target-based normalization technique for materials selection. Materials & Design, 35, 647-654. https://doi.org/10.1016/j.matdes.2011.09.005Jahan, A., Edwards, K.L. (2013). VIKOR method for material selection problems with interval numbers and target-based criteria. Materials & Design, 47, 759-765. https://doi.org/10.1016/j.matdes.2012.12.072Jahan, A., Edwards, K.L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design, 65, 335-342. https://doi.org/10.1016/j.matdes.2014.09.022Jahan, A., Edwards, K.L., Bahraminasab, M. (2016). Multi-criteria decision analysis for supporting the selection of engineering materials in product design, Oxford, Butterworth-Heinemann.Jahan, A., Mustapha, F., Ismail, M.Y., Sapuan, S.M., Bahraminasab, M. (2011). A comprehensive VIKOR method for material selection. Materials & Design, 32, 1215-1221. https://doi.org/10.1016/j.matdes.2010.10.015Jahan, A., Zavadskas, E.K. (2018). ELECTRE-IDAT for design decision-making problems with interval data and target-based criteria. Soft Computing, 23, 129-143. https://doi.org/10.1007/s00500-018-3501-6Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Davoodi, A.R. (2009). Extension of TOPSIS for decision-making problems with interval data: Interval efficiency. Mathematical and Computer Modelling, 49, 1137-1142. https://doi.org/10.1016/j.mcm.2008.07.009Jahanshahloo, G.R., Lotfi, F.H., Izadikhah, M. (2006). An algorithmic method to extend TOPSIS for decision-making problems with interval data. 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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

Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.

open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the experts’ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of ‘Technology’, ‘Quality’, and ‘Operation’ have respectively the highest importance. Furthermore, the strategies for “new business models development’, ‘Improving information systems’ and ‘Human resource management’ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information

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