The multi-attribute group decision making method based on the interval grey linguistic variables weighted harmonic aggregation operators

With respect to the characteristics of fuzziness, complexity and uncertainty for many group-decision making problems in real world, the paper proposes a novel method based on the interval grey linguistic variables hybrid weighted harmonic aggregation operators to solve the multiple attribute group decision making problems in which the attribute values and the weights take the form of the interval grey linguistic variables. In the approach, the relative concepts and the operation rules of interval grey linguistic variables are defined, and some operators (such as interval grey linguistic weighted harmonic aggregation (IGLWHA) operator, interval grey linguistic ordered weighted harmonic aggregation (IGLOWHA) operator, and interval grey linguistic hybrid weighted harmonic aggregation (IGLHWHA) operator) are proposed to solve the group decision making problems. The computational results from an illustrative example have shown that the proposed approach is feasible and effective for the group-decision making problems

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). The analytic hierarchy process with interval preference statements. Omega, 67, 177-185. https://doi.org/10.1016/j.omega.2016.05.004Alemi-Ardakani, M., Milani, A.S., Yannacopoulos, S., Shokouhi, G. (2016). On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites. Expert Systems With Applications, 46, 426-438. https://doi.org/10.1016/j.eswa.2015.11.003Amiri, M., Nosratian, N.E., Jamshidi, A., Kazemi, A. (2008). Developing a new ELECTRE method with interval data in multiple attribute decision making problems. Journal of Applied Sciences, 8, 4017-4028. https://doi.org/10.3923/jas.2008.4017.4028Bahraminasab, M., Jahan, A. (2011). Material selection for femoral component of total knee replacement using comprehensive VIKOR. Materials & Design, 32, 4471-4477. https://doi.org/10.1016/j.matdes.2011.03.046Baradaran, V., Azarnia, S. (2013). An Approach to Test Consistency and Generate Weights from Grey Pairwise Matrices in Grey Analytical Hierarchy Process. Journal of Grey System, 25.Behzadian, M., Otaghsara, S.K., Yazdani, M., Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39, 13051-13069. https://doi.org/10.1016/j.eswa.2012.05.056Cables, E., Lamata, M.T., Verdegay, J.L. (2018). FRIM-Fuzzy Reference Ideal Method in Multicriteria Decision Making. In Collan, M. & Kacprzyk, J. (Eds.) Soft Computing Applications for Group Decision-making and Consensus Modeling. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-319-60207-3_19Çakır, S. (2016). An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design. Journal of Intelligent Manufacturing, 1-13. https://doi.org/10.1007/s10845-015-1189-3Celen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market. Informatica, 25, 185-208. https://doi.org/10.15388/Informatica.2014.10Celik, E., Erdogan, M., Gumus, A. (2016). An extended fuzzy TOPSIS-GRA method based on different separation measures for green logistics service provider selection. International Journal of Environmental Science and Technology, 13, 1377-1392. https://doi.org/10.1007/s13762-016-0977-4Dymova, L., Sevastjanov, P., Tikhonenko, A. (2013). A direct interval extension of TOPSIS method. Expert Systems With Applications, 40, 4841-4847. https://doi.org/10.1016/j.eswa.2013.02.022Garca-Cascales, M.S., Lamata, M.T. (2012). On rank reversal and TOPSIS method. Mathematical and Computer Modelling, 56, 123-132. https://doi.org/10.1016/j.mcm.2011.12.022Hafezalkotob, A., Hafezalkotob, A. (2015). 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. Applied Mathematical Modelling, 40, 1372-1386. https://doi.org/10.1016/j.apm.2015.07.019Hajiagha, S.H.R., Hashemi, S.S., Zavadskas, E.K., Akrami, H. (2012). Extensions of LINMAP model for multi criteria decision making with grey numbers. Technological and Economic Development of Economy, 18, 636-650. https://doi.org/10.3846/20294913.2012.740518Hazelrigg, G.A. (2003). Validation of engineering design alternative selection methods. Engineering Optimization, 35, 103-120. https://doi.org/10.1080/0305215031000097059Hu, J., Du, Y., Mo, H., Wei, D., Deng, Y. (2016). A modified weighted TOPSIS to identify influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 444, 73-85. https://doi.org/10.1016/j.physa.2015.09.028Huang, Y., Jiang, W. (2018). Extension of TOPSIS Method and its Application in Investment. Arabian Journal for Science and Engineering, 43, 693-705. https://doi.org/10.1007/s13369-017-2736-3Jahan, A. (2018). 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. Applied Mathematics and Computation, 175, 1375-1384. https://doi.org/10.1016/j.amc.2005.08.048Kasirian, M., Yusuff, R. (2013). An integration of a hybrid modified TOPSIS with a PGP model for the supplier selection with interdependent criteria. International Journal of Production Research, 51, 1037-1054. https://doi.org/10.1080/00207543.2012.663107Kuo, T. (2017). A modified TOPSIS with a different ranking index. European Journal of Operational Research, 260, 152-160. https://doi.org/10.1016/j.ejor.2016.11.052Liang, D., Xu, Z. (2017). The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Applied Soft Computing, 60, 167-179. https://doi.org/10.1016/j.asoc.2017.06.034Liao, H., Wu, X. (2019). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega, 94. 102058. https://doi.org/10.1016/j.omega.2019.04.001Liu, H.C., You, J.X., Zhen, L., Fan, X.J. (2014). A novel hybrid multiple criteria decision making model for material selection with targetbased criteria. Materials & Design, 60, 380-390. https://doi.org/10.1016/j.matdes.2014.03.071Maghsoodi, A.I., Maghsoodi, A.I., Poursoltan, P., Antucheviciene, J., Turskis, Z. (2019). Dam construction material selection by implementing the integrated SWARA-CODAS approach with target-based attributes. Archives of Civil and Mechanical Engineering, 19, 1194-1210. https://doi.org/10.1016/j.acme.2019.06.010Milani, A.S., Shanian, A., Madoliat, R., Nemes, J.A. (2005). The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection. Structural and Multidisciplinary Optimization, 29, 312-318. https://doi.org/10.1007/s00158-004-0473-1Peldschus, F. (2009). The analysis of the quality of the results obtained with the methods of multi-criteria decisions. Technological and Economic Development of Economy, 15, 580-592. https://doi.org/10.3846/1392-8619.2009.15.580-592Peldschus, F. (2018). Recent findings from numerical analysis in multi-criteria decision making. Technological and Economic Development of Economy, 24, 1695-1717. https://doi.org/10.3846/20294913.2017.1356761Perez, E.C., Lamata, M., Verdegay, J. (2016). RIM-Reference Ideal Method in Multicriteria Decision Making. Information Sciences, 337- 338, 1-10. https://doi.org/10.1016/j.ins.2015.12.011Sayadi, M.K., Heydari, M., Shahanaghi, K. (2009). Extension of VIKOR method for decision making problem with interval numbers. Applied Mathematical Modelling, 33, 2257-2262. https://doi.org/10.1016/j.apm.2008.06.002Sen, P., Yang, J.B. (1998). MCDM and the Nature of Decision Making in Design, Springer. https://doi.org/10.1007/978-1-4471-3020-8_2Sevastianov, P. (2007). Numerical methods for interval and fuzzy number comparison based on the probabilistic approach and Dempster- Shafer theory. Information Sciences, 177, 4645-4661. https://doi.org/10.1016/j.ins.2007.05.001Shanian, A., Savadogo, O. (2009). A methodological concept for material selection of highly sensitive components based on multiple criteria decision analysis. Expert Systems With Applications, 36, 1362-1370. https://doi.org/10.1016/j.eswa.2007.11.052Shen, F., Ma, X., Li, Z., Xu, Z., Cai, D. (2018). An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation. Information Sciences, 428, 105-119. https://doi.org/10.1016/j.ins.2017.10.045Shishank, S., Dekkers, R. (2013). Outsourcing: decision-making methods and criteria during design and engineering. Production Planning & Control, 24, 318-336. https://doi.org/10.1080/09537287.2011.648544Shouzhen, Z., Yao, X. (2018). A method based on TOPSIS and distance measures for hesitant fuzzy multiple attribute decision making. Technological and Economic Development of Economy, 24, 969-983. https://doi.org/10.3846/20294913.2016.1216472Stanujkic, D., Magdalinovic, N., Jovanovic, R., Stojanovic, S. (2012). An objective multi-criteria approach to optimization using MOORA method and interval grey numbers. Technological and Economic Development of Economy, 18, 331-363. https://doi.org/10.3846/20294913.2012.676996Suder, A., Kahraman, C. (2018). Multiattribute evaluation of organic and inorganic agricultural food investments using fuzzy TOPSIS. Technological and Economic Development of Economy, 24, 844-858. https://doi.org/10.3846/20294913.2016.1216905Tilstra, A.H., Backlund, P.B., Seepersad, C.C., Wood, K.L. (2015). Principles for designing products with flexibility for future evolution. International Journal of Mass Customisation, 5, 22-54. https://doi.org/10.1504/IJMASSC.2015.069597Tsaur, R.C. (2011) Decision risk analysis for an interval TOPSIS method. Applied Mathematics and Computation, 218, 4295-4304. https://doi.org/10.1016/j.amc.2011.10.001Turskis, Z., Zavadskas, E.K. (2010) A novel method for multiple criteria analysis: grey additive ratio assessment (ARAS-G) method. Informatica, 21, 597-610. https://doi.org/10.15388/Informatica.2010.307Wang, Y.M., Luo, Y. (2009) On rank reversal in decision analysis. Mathematical and Computer Modelling, 49, 1221-1229. https://doi.org/10.1016/j.mcm.2008.06.019Ye, J. (2015) An extended TOPSIS method for multiple attribute group decision making based on single valued neutrosophic linguistic numbers. Journal of Intelligent & Fuzzy Systems, 28, 247-255. https://doi.org/10.3233/IFS-141295Yue, Z. (2013) Group decision making with multi-attribute interval data. Information Fusion, 14, 551-561. https://doi.org/10.1016/j.inffus.2013.01.00

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Intertemporal Choice of Fuzzy Soft Sets

This paper first merges two noteworthy aspects of choice. On the one hand, soft sets and fuzzy soft sets are popular models that have been largely applied to decision making problems, such as real estate valuation, medical diagnosis (glaucoma, prostate cancer, etc.), data mining, or international trade. They provide crisp or fuzzy parameterized descriptions of the universe of alternatives. On the other hand, in many decisions, costs and benefits occur at different points in time. This brings about intertemporal choices, which may involve an indefinitely large number of periods. However, the literature does not provide a model, let alone a solution, to the intertemporal problem when the alternatives are described by (fuzzy) parameterizations. In this paper, we propose a novel soft set inspired model that applies to the intertemporal framework, hence it fills an important gap in the development of fuzzy soft set theory. An algorithm allows the selection of the optimal option in intertemporal choice problems with an infinite time horizon. We illustrate its application with a numerical example involving alternative portfolios of projects that a public administration may undertake. This allows us to establish a pioneering intertemporal model of choice in the framework of extended fuzzy set theorie

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

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

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data