123,902 research outputs found

    Development of group decision making model under fuzzy environment

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    Multi criteria group decision making (MCGDM) methods are broadly used in the real-world decision circumstances for homogeneous groups. Some decision-makers’viewpoints at times are more important or reliable than others, or they may differ in terms of the decision-maker experience, education, expertise and other aspects. Thus, a heterogeneous group of decision makers with dissimilar members has to be composed in MCGDM. Multi-dimensional personnel evaluation is one of the most critical decisions to make in order to achieve the organization goals. In many situations, raters may decide on the basis of imprecise information coming from a variety of sources about ratee with respect to criteria. In fact, some criteria are completely quantifiable, some partially quantifiable, and others completely subjective; moreover crisp data is inappropriate to model real-world circumstances. Linguistic labels or fuzzy preferences are therefore, used to deal with uncertain and inaccurate factors involved and seem more reliable in complex group decision situations. In this research, heterogeneous group decision making models under fuzzy environment for multi-dimensional personnel evaluation were proposed to compensate the differences of decision makers’ knowledge such as: education,expertise, experience and other aspects. A new fuzzy group decision making method was developed under the linguistic framework for heterogeneous group decision making that aims at a desired consensus. The method allocates different weights for each decision maker using linguistic terms to express their fuzzy preferences for alternative solutions and for individual judgments. Besides, the classical ordinal approach method under a linguistic framework is developed for heterogeneous group decision making, which allows group members to express their fuzzy preferences in linguistic terms for alternative selection and for individual judgments. Furthermore, a fuzzy extension of technique for order preference by similarity to ideal solution (TOPSIS) method under fuzzy environment was proposed. The method covers heterogeneous group decision making by considering the decision makers’ viewpoint weights. In order to solve the problem of discrepancy between decision making methods’ results, a new optimization method was developed, to aggregate the results’of different decision making models. The four proposed methods were used in a case study. Proposed methods focused on the implementation of fuzzy logic approach in the personnel evaluation system. Furthemore, personnel were evaluated from different points of view (supervisors,colleagues, inferiors and employee him/herself). A fuzzy Delphi method and linguistic terms represented by the fuzzy numbers were developed to elicit qualitative and quantitative criteria and assess criteria weights and relative importance of evaluation group’s viewpoints. Then, the proposed methods’ results were compared to already established methods. The study identified that the results of the proposed methods are closely related to other methods and the selections made by the proposed methods approximately are identical with the other already established methods. The Spearman’s rank correlation coefficient shows highly consistent rankings obtained by the methods. No significant difference in the ranking of the proposed methods and the other established methods was observed. The results of the problems solution based on the aggregated proposed model show that the aggregated model achieved the highest value in the Spearman’s rank correlation compared to the average method and Copeland function. Furthermore, the high Spearman’s rank correlation coefficient between the rankings supports the consistency of the results and similarity of applicability of the methods

    Refractured Well Selection for Multicriteria Group Decision Making by Integrating Fuzzy AHP with Fuzzy TOPSIS Based on Interval-Typed Fuzzy Numbers

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    Multicriteria group decision making (MCGDM) research has rapidly been developed and become a hot topic for solving complex decision problems. Because of incomplete or non-obtainable information, the refractured well-selection problem often exists in complex and vague conditions that the relative importance of the criteria and the impacts of the alternatives on these criteria are difficult to determine precisely. This paper presents a new model for MCGDM by integrating fuzzy analytic hierarchy process (AHP) with fuzzy TOPSIS based on interval-typed fuzzy numbers, to help group decision makers for well-selection during refracturing treatment. The fuzzy AHP is used to analyze the structure of the selection problem and to determine weights of the criteria with triangular fuzzy numbers, and fuzzy TOPSIS with interval-typed triangular fuzzy numbers is proposed to determine final ranking for all the alternatives. Furthermore, the algorithm allows finding the best alternatives. The feasibility of the proposed methodology is also demonstrated by the application of refractured well-selection problem and the method will provide a more effective decision-making tool for MCGDM problems

    Using Pythagorean Fuzzy Sets (PFS) in Multiple Criteria Group Decision Making (MCGDM) Methods for Engineering Materials Selection Applications

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    The process of materials’ selection is very critical during the initial stages of designing manufactured products. Inefficient decision-making outcomes in the material selection process could result in poor quality of products and unnecessary costs. In the last century, numerous materials have been developed for manufacturing mechanical components in different industries. Many of these new materials are similar in their properties and performances, thus creating great challenges for designers and engineers to make accurate selections. Our main objective in this work is to assist decision makers (DMs) within the manufacturing field to evaluate materials alternatives and to select the best alternative for specific manufacturing purposes. In this research, new hybrid fuzzy Multiple Criteria Group Decision Making (MCGDM) methods are proposed for the material selection problem. The proposed methods tackle some challenges that are associated with the material selection decision making process, such as aggregating decision makers’ (DMs) decisions appropriately and modeling uncertainty. In the proposed hybrid models, a novel aggregation approach is developed to convert DMs crisp decisions to Pythagorean fuzzy sets (PFS). This approach gives more flexibility to DMs to express their opinions than the traditional fuzzy and intuitionistic sets (IFS). Then, the proposed aggregation approach is integrated with a ranking method to solve the Pythagorean Fuzzy Multi Criteria Decision Making (PFMCGDM) problem and rank the material alternatives. The ranking methods used in the hybrid models are the Pythagorean Fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and Pythagorean Fuzzy COPRAS (COmplex PRoportional Assessment). TOPSIS and COPRAS are selected based on their effectiveness and practicality in dealing with the nature of material selection problems. In the aggregation approach, the Sugeno Fuzzy measure and the Shapley value are used to fairly distribute the DMs weight in the Pythagorean Fuzzy numbers. Additionally, new functions to calculate uncertainty from DMs recommendations are developed using the Takagai-Sugeno approach. The literature reveals some work on these methods, but to our knowledge, there are no published works that integrate the proposed aggregation approach with the selected MCDM ranking methods under the Pythagorean Fuzzy environment for the use in materials selection problems. Furthermore, the proposed methods might be applied, due to its novelty, to any MCDM problem in other areas. A practical validation of the proposed hybrid PFMCGDM methods is investigated through conducting a case study of material selection for high pressure turbine blades in jet engines. The main objectives of the case study were: 1) to investigate the new developed aggregation approach in converting real DMs crisp decisions into Pythagorean fuzzy numbers; 2) to test the applicability of both the hybrid PFMCGDM TOPSIS and the hybrid PFMCGDM COPRAS methods in the field of material selection. In this case study, a group of five DMs, faculty members and graduate students, from the Materials Science and Engineering Department at the University of Wisconsin-Milwaukee, were selected to participate as DMs. Their evaluations fulfilled the first objective of the case study. A computer application for material selection was developed to assist designers and engineers in real life problems. A comparative analysis was performed to compare the results of both hybrid MCGDM methods. A sensitivity analysis was conducted to show the robustness and reliability of the outcomes obtained from both methods. It is concluded that using the proposed hybrid PFMCGDM TOPSIS method is more effective and practical in the material selection process than the proposed hybrid PFMCGDM COPRAS method. Additionally, recommendations for further research are suggested

    Decision making with Dempster-Shafer belief structure and the OWAWA operator

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    [EN] A new decision making model that uses the weighted average and the ordered weighted averaging (OWA) operator in the Dempster-Shafer belief structure is presented. Thus, we are able to represent the decision making problem considering objective and subjective information and the attitudinal character of the decision maker. For doing so, we use the ordered weighted averaging ¿ weighted average (OWAWA) operator. It is an aggregation operator that unifies the weighted average and the OWA in the same formulation. This approach is generalized by using quasi-arithmetic means and group decision making techniques. An application of the new approach in a group decision making problem concerning political management of a country is also developed.We would like to thank the anonymous reviewers for valuable comments that have improved the quality of the paper. Support from the Spanish Ministry of Education under project JC2009-00189 , the University of Barcelona (099311) and the European Commission (PIEFGA-2011-300062) is gratefully acknowledgedMerigó, JM.; Engemann, KJ.; Palacios Marqués, D. (2013). Decision making with Dempster-Shafer belief structure and the OWAWA operator. Technological and Economic Development of Economy. 19(sup 1):S100-S118. https://doi.org/10.3846/20294913.2013.869517SS100S11819sup 1Antuchevičienė, J., Zavadskas, E. K., & Zakarevičius, A. (2010). MULTIPLE CRITERIA CONSTRUCTION MANAGEMENT DECISIONS CONSIDERING RELATIONS BETWEEN CRITERIA / DAUGIATIKSLIAI STATYBOS VALDYMO SPRENDIMAI ATSIŽVELGIANT Į RODIKLIŲ TARPUSAVIO PRIKLAUSOMYBĘ. Technological and Economic Development of Economy, 16(1), 109-125. doi:10.3846/tede.2010.07Brauers, W. K. M., & Zavadskas, E. K. (2010). PROJECT MANAGEMENT BY MULTIMOORA AS AN INSTRUMENT FOR TRANSITION ECONOMIES / PROJEKTŲ VADYBA SU MULTIMOORA KAIP PRIEMONĖ PEREINAMOJO LAIKOTARPIO ŪKIAMS. Technological and Economic Development of Economy, 16(1), 5-24. doi:10.3846/tede.2010.01Dempster, A. P. (1967). Upper and Lower Probabilities Induced by a Multivalued Mapping. The Annals of Mathematical Statistics, 38(2), 325-339. doi:10.1214/aoms/1177698950ENGEMANN, K. J., MILLER, H. E., & YAGER, R. R. (1996). DECISION MAKING WITH BELIEF STRUCTURES: AN APPLICATION IN RISK MANAGEMENT. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 04(01), 1-25. doi:10.1142/s0218488596000020ENGEMANN, K. J., FILEV, D. P., & YAGER, R. R. (1996). MODELLING DECISION MAKING USING IMMEDIATE PROBABILITIES. International Journal of General Systems, 24(3), 281-294. doi:10.1080/03081079608945123Engemann, K. J., & Miller, H. E. (2009). Critical infrastructure and smart technology risk modelling using computational intelligence. International Journal of Business Continuity and Risk Management, 1(1), 91. doi:10.1504/ijbcrm.2009.028953Fodor, J., Marichal, J.-L., & Roubens, M. (1995). Characterization of the ordered weighted averaging operators. IEEE Transactions on Fuzzy Systems, 3(2), 236-240. doi:10.1109/91.388176Han, Z., & Liu, P. (2011). A FUZZY MULTI-ATTRIBUTE DECISION-MAKING METHOD UNDER RISK WITH UNKNOWN ATTRIBUTE WEIGHTS / NERAIŠKUSIS MAŽESNĖS RIZIKOS DAUGIATIKSLIS SPRENDIMŲ PRIĖMIMO METODAS SU NEŽINOMAIS PRISKIRIAMAIS REIKŠMINGUMAIS. Technological and Economic Development of Economy, 17(2), 246-258. doi:10.3846/20294913.2011.580575Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). SELECTION OF RATIONAL DISPUTE RESOLUTION METHOD BY APPLYING NEW STEP‐WISE WEIGHT ASSESSMENT RATIO ANALYSIS (SWARA). Journal of Business Economics and Management, 11(2), 243-258. doi:10.3846/jbem.2010.12Liu, P. (2009). MULTI‐ATTRIBUTE DECISION‐MAKING METHOD RESEARCH BASED ON INTERVAL VAGUE SET AND TOPSIS METHOD. Technological and Economic Development of Economy, 15(3), 453-463. doi:10.3846/1392-8619.2009.15.453-463Liu, P. (2011). A weighted aggregation operators multi-attribute group decision-making method based on interval-valued trapezoidal fuzzy numbers. Expert Systems with Applications, 38(1), 1053-1060. doi:10.1016/j.eswa.2010.07.144Merigó, J. M. (2011). A unified model between the weighted average and the induced OWA operator. Expert Systems with Applications, 38(9), 11560-11572. doi:10.1016/j.eswa.2011.03.034Merigó, J. M. (2012). The probabilistic weighted average and its application in multiperson decision making. International Journal of Intelligent Systems, 27(5), 457-476. doi:10.1002/int.21531Merigó, J. M., & Casanovas, M. (2009). Induced aggregation operators in decision making with the Dempster-Shafer belief structure. International Journal of Intelligent Systems, 24(8), 934-954. doi:10.1002/int.20368Merigó, J. M., & Casanovas, M. (2010). The uncertain induced quasi-arithmetic OWA operator. International Journal of Intelligent Systems, 26(1), 1-24. doi:10.1002/int.20444MERIGÓ, J. M., & CASANOVAS, M. (2011). THE UNCERTAIN GENERALIZED OWA OPERATOR AND ITS APPLICATION TO FINANCIAL DECISION MAKING. International Journal of Information Technology & Decision Making, 10(02), 211-230. doi:10.1142/s0219622011004300MERIGÓ, J. M., CASANOVAS, M., & MARTÍNEZ, L. (2010). LINGUISTIC AGGREGATION OPERATORS FOR LINGUISTIC DECISION MAKING BASED ON THE DEMPSTER-SHAFER THEORY OF EVIDENCE. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 18(03), 287-304. doi:10.1142/s0218488510006544MERIGO, J., & GILLAFUENTE, A. (2009). The induced generalized OWA operator. Information Sciences, 179(6), 729-741. doi:10.1016/j.ins.2008.11.013Merigó, J. M., & Gil-Lafuente, A. M. (2010). New decision-making techniques and their application in the selection of financial products. Information Sciences, 180(11), 2085-2094. doi:10.1016/j.ins.2010.01.028Merigó, J. M., & Wei, G. (2011). PROBABILISTIC AGGREGATION OPERATORS AND THEIR APPLICATION IN UNCERTAIN MULTI-PERSON DECISION-MAKING / TIKIMYBINIAI SUMAVIMO OPERATORIAI IR JŲ TAIKYMAS PRIIMANT GRUPINIUS SPRENDIMUS NEAPIBRĖŽTOJE APLINKOJE. Technological and Economic Development of Economy, 17(2), 335-351. doi:10.3846/20294913.2011.584961Podvezko, V. (2009). Application of AHP technique. Journal of Business Economics and Management, 10(2), 181-189. doi:10.3846/1611-1699.2009.10.181-189Reformat, M., & Yager, R. R. (2007). Building ensemble classifiers using belief functions and OWA operators. Soft Computing, 12(6), 543-558. doi:10.1007/s00500-007-0227-2Srivastava, R. P., & Mock, T. J. (Eds.). (2002). Belief Functions in Business Decisions. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-7908-1798-0Torra, V. (1997). The weighted OWA operator. International Journal of Intelligent Systems, 12(2), 153-166. doi:10.1002/(sici)1098-111x(199702)12:23.0.co;2-pWei, G.-W. (2011). Some generalized aggregating operators with linguistic information and their application to multiple attribute group decision making. Computers & Industrial Engineering, 61(1), 32-38. doi:10.1016/j.cie.2011.02.007Wei, G., Zhao, X., & Lin, R. (2010). Some Induced Aggregating Operators with Fuzzy Number Intuitionistic Fuzzy Information and their Applications to Group Decision Making. International Journal of Computational Intelligence Systems, 3(1), 84-95. doi:10.1080/18756891.2010.9727679Xu, Z. (2005). An overview of methods for determining OWA weights. International Journal of Intelligent Systems, 20(8), 843-865. doi:10.1002/int.20097Xu, Z. (2009). A Deviation-Based Approach to Intuitionistic Fuzzy Multiple Attribute Group Decision Making. Group Decision and Negotiation, 19(1), 57-76. doi:10.1007/s10726-009-9164-zXu, Z. S., & Da, Q. L. (2003). An overview of operators for aggregating information. International Journal of Intelligent Systems, 18(9), 953-969. doi:10.1002/int.10127Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183-190. doi:10.1109/21.87068YAGER, R. R. (1992). DECISION MAKING UNDER DEMPSTER-SHAFER UNCERTAINTIES. International Journal of General Systems, 20(3), 233-245. doi:10.1080/03081079208945033Yager, R. R. (1993). Families of OWA operators. Fuzzy Sets and Systems, 59(2), 125-148. doi:10.1016/0165-0114(93)90194-mYager, R. R. (1998). Including importances in OWA aggregations using fuzzy systems modeling. IEEE Transactions on Fuzzy Systems, 6(2), 286-294. doi:10.1109/91.669028Yager, R. R. (2004). Generalized OWA Aggregation Operators. Fuzzy Optimization and Decision Making, 3(1), 93-107. doi:10.1023/b:fodm.0000013074.68765.97Yager, R. R., Engemann, K. J., & Filev, D. P. (1995). On the concept of immediate probabilities. International Journal of Intelligent Systems, 10(4), 373-397. doi:10.1002/int.4550100403Yager, R. R., & Kacprzyk, J. (Eds.). (1997). The Ordered Weighted Averaging Operators. doi:10.1007/978-1-4615-6123-1Yager, R. R., Kacprzyk, J., & Beliakov, G. (Eds.). (2011). Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-642-17910-5Yager, R. R., & Liu, L. (Eds.). (2008). Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-540-44792-4Zavadskas, E. K., & Turskis, Z. (2011). MULTIPLE CRITERIA DECISION MAKING (MCDM) METHODS IN ECONOMICS: AN OVERVIEW / DAUGIATIKSLIAI SPRENDIMŲ PRIĖMIMO METODAI EKONOMIKOJE: APŽVALGA. Technological and Economic Development of Economy, 17(2), 397-427. doi:10.3846/20294913.2011.593291Zavadskas, E. K., Vilutienė, T., Turskis, Z., & Tamosaitienė, J. (2010). CONTRACTOR SELECTION FOR CONSTRUCTION WORKS BY APPLYING SAW‐G AND TOPSIS GREY TECHNIQUES. Journal of Business Economics and Management, 11(1), 34-55. doi:10.3846/jbem.2010.03Zeng, S., & Su, W. (2011). Intuitionistic fuzzy ordered weighted distance operator. Knowledge-Based Systems, 24(8), 1224-1232. doi:10.1016/j.knosys.2011.05.013Zhang, X., & Liu, P. (2010). METHOD FOR AGGREGATING TRIANGULAR FUZZY INTUITIONISTIC FUZZY INFORMATION AND ITS APPLICATION TO DECISION MAKING / NUMANOMŲ NEAPIBRĖŽTŲJŲ AIBIŲ TEORIJA IR JOS TAIKYMAS PRIIMANT SPRENDIMUS. Technological and Economic Development of Economy, 16(2), 280-290. doi:10.3846/tede.2010.18Zhao, H., Xu, Z., Ni, M., & Liu, S. (2010). Generalized aggregation operators for intuitionistic fuzzy sets. International Journal of Intelligent Systems, 25(1), 1-30. doi:10.1002/int.20386Zhou, L.-G., & Chen, H. (2010). Generalized ordered weighted logarithm aggregation operators and their applications to group decision making. International Journal of Intelligent Systems, n/a-n/a. doi:10.1002/int.20419Zhou, L.-G., & Chen, H.-Y. (2011). Continuous generalized OWA operator and its application to decision making. Fuzzy Sets and Systems, 168(1), 18-34. doi:10.1016/j.fss.2010.05.009Zhou, L., & Chen, H. (2012). A generalization of the power aggregation operators for linguistic environment and its application in group decision making. Knowledge-Based Systems, 26, 216-224. doi:10.1016/j.knosys.2011.08.004Zhou, L., Chen, H., & Liu, J. (2011). Generalized Multiple Averaging Operators and their Applications to Group Decision Making. Group Decision and Negotiation, 22(2), 331-358. doi:10.1007/s10726-011-9267-1Zhou, L., Chen, H., & Liu, J. (2012). Generalized power aggregation operators and their applications in group decision making. Computers & Industrial Engineering, 62(4), 989-999. doi:10.1016/j.cie.2011.12.025Zhou, L.-G., Chen, H.-Y., Merigó, J. M., & Gil-Lafuente, A. M. (2012). Uncertain generalized aggregation operators. Expert Systems with Applications, 39(1), 1105-1117. doi:10.1016/j.eswa.2011.07.11

    Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building.

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    The residential sector is well known to be one of the main energy consumers worldwide. The purpose of this study is to select the best renewable energy alternatives for electricity generation in a residential building by using a new integrated fuzzy multi-criteria group decision-making method. In renewable energy decision-making problems, the preferences of experts and decision-makers are generally uncertain. Furthermore, it is challenging to quantify the reel performance of renewable energy alternatives using a set of exact values. Fuzzy logic is commonly applied to deal with those uncertainties. The method proposed in this paper combines different methods. First, the Delphi method is used in order to select a preliminary set of renewable energy alternatives for electricity generation as well as a preliminary set of criteria (economic, environmental, social, etc.). Then, the questionnaire is used to study the renewable energy alternatives preferences of the residents of the residential building’. Later, the FAHP (Fuzzy Analytical Hierarchy Process) is implemented to obtain the weighs of the criteria taking into consideration uncertainties in expert's judgments. Finally, the FPROMETHEE (Fuzzy Preference Ranking Organization Method for Enrichment Evaluation) global ranking is performed in order to get a complete ranking of the renewable energy alternatives taking into account uncertainties related to the alternatives' evaluations. The originality of this paper comes from the application of the proposed integrated Delphi- FAHP- FPROMETHEE methodology for the selection of the best renewable energy alternatives for electricity generation in a residential building. A case study has validated the effectiveness and the applicability of the proposed method. The results reveal that the proposed integrated method helps to formulate the problem and is particularly effective in handling uncertain data. It facilitates the selection of the best renewable energy alternatives in a manner that is participatory, comprehensive, robust, and reliable

    Proposing a new methodology for prioritising the investment strategies in the private sector of Iran

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    This article proposes a systematic and organised approach for group decision-making in the presence of the uncertainty involved in expert judgments as used in multi-criteria decision-making (MCDM) issues. This procedure comprises the selection of the optimum alternative with respect to the evaluation criteria under consideration, in particular to select the strategy of investing. However, the selection of the investment strategy is difficult on account of considering the numerous quantitative and qualitative parameters like benefits, opportunities, costs, and risks. However, it is possible that these parameters have a significant influence on each other. A decision-making trial and evaluation laboratory (DEMATEL), used to define the influential network of elements, can be employed to construct a network relationship map (NRM). On the other hand, according to whether the information is incomplete or unavailable, uncertainty is an inseparable part of making decision for solving the MCDM problems. Therefore, this article proposes a new hybrid model based on analytic hierarchical process (AHP), DEMATEL, and echnique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques under fuzzy environment to evaluate the problem of the selection of the investment strategy. To achieve the aim, a three-step process is presented to solve a sophisticated problem. First, the AHP method is employed to break down the investment problem into simple structure and calculate the importance weights of criteria by using a pairwise comparison process. Second, the DEMATEL technique is applied for considering interdependence and dependencies and computing the global weights of benefit, opportunities, cost, and risk (BOCR) factors. Finally, the fuzzy TOPSIS methodology is used for prioritising the possible alternatives. To demonstrate the potential application of the proposed model, a numerical example is illustrated and investigated. The results show that the proposed model has a high ability to prioritise the strategies of investing

    New group decision making method in intuitionistic fuzzy setting based on TOPSIS

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    In multiple attribute group decision making, the weights of decision makers are very crucial to ranking results and have gained more and more attentions. A new approach to determining experts’ weights is proposed based on the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method in intuitionistic fuzzy setting. The weights determined by our method have two advantages: the evaluation value has a large weight if it is close to the positive ideal evaluation value and far from negative ideal evaluation values at the same time, otherwise it is assigned a small weight; experts have different weights for different attributes, which are more appropriate for real decision making problems since each expert has his/her own knowledge and expertise. The multiple attribute intuitionistic fuzzy group decision making algorithm has been proposed which is suitable for different situations about the attribute weight information, including the attribute weights are known exactly, partly known and unknown completely. A supplier selection problem and the evaluation of murals in a metro line are finally used to illustrate the feasibility, efficiency and practical advantages of the developed approaches

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

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    [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

    Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices

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    [EN] After the recent establishment of the Sustainable Development Goals and the Agenda 2030, the sustainable design of products in general and infrastructures in particular emerge as a challenging field for the development and application of multicriteria decision-making tools. Sustainability-related decision problems usually involve, by definition, a wide variety in number and nature of conflicting criteria, thus pushing the limits of conventional multicriteria decision-making tools practices. The greater the number of criteria and the more complex the relations existing between them in a decisional problem, the less accurate and certain are the judgments required by usual methods, such as the analytic hierarchy process (AHP). The present paper proposes a neutrosophic AHP completion methodology to reduce the number of judgments required to be emitted by the decision maker. This increases the consistency of their responses, while accounting for uncertainties associated to the fuzziness of human thinking. The method is applied to a sustainable-design problem, resulting in weight estimations that allow for a reduction of up to 22% of the conventionally required comparisons, with an average accuracy below 10% between estimates and the weights resulting from a conventionally completed AHP matrix, and a root mean standard error below 15%.The authors acknowledge the financial support of the Spanish Ministry of Economy and Business, along with FEDER funding (DIMALIFE Project: BIA2017-85098-R).Navarro, IJ.; Martí Albiñana, JV.; Yepes, V. (2021). Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices. Mathematics. 9(5):1-19. https://doi.org/10.3390/math905049611995Worrell, E., Price, L., Martin, N., Hendriks, C., & Meida, L. O. (2001). CARBON DIOXIDE EMISSIONS FROM THE GLOBAL CEMENT INDUSTRY. Annual Review of Energy and the Environment, 26(1), 303-329. doi:10.1146/annurev.energy.26.1.303García, J., Yepes, V., & Martí, J. V. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics, 8(4), 555. doi:10.3390/math8040555Penadés-Plà, V., García-Segura, T., & Yepes, V. (2020). Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge. Mathematics, 8(3), 398. doi:10.3390/math8030398Kim, S., & Frangopol, D. M. (2017). Multi-objective probabilistic optimum monitoring planning considering fatigue damage detection, maintenance, reliability, service life and cost. Structural and Multidisciplinary Optimization, 57(1), 39-54. doi:10.1007/s00158-017-1849-3García-Segura, T., Yepes, V., & Frangopol, D. M. (2017). Multi-objective design of post-tensioned concrete road bridges using artificial neural networks. Structural and Multidisciplinary Optimization, 56(1), 139-150. doi:10.1007/s00158-017-1653-0Van den Heede, P., & De Belie, N. (2014). A service life based global warming potential for high-volume fly ash concrete exposed to carbonation. Construction and Building Materials, 55, 183-193. doi:10.1016/j.conbuildmat.2014.01.033García, J., Martí, J. V., & Yepes, V. (2020). The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm. Mathematics, 8(6), 862. doi:10.3390/math8060862García-Segura, T., Penadés-Plà, V., & Yepes, V. (2018). Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty. Journal of Cleaner Production, 202, 904-915. doi:10.1016/j.jclepro.2018.08.177Gursel, A. P., & Ostertag, C. (2016). Comparative life-cycle impact assessment of concrete manufacturing in Singapore. The International Journal of Life Cycle Assessment, 22(2), 237-255. doi:10.1007/s11367-016-1149-yPenadés-Plà, V., Martí, J. V., García-Segura, T., & Yepes, V. (2017). Life-Cycle Assessment: A Comparison between Two Optimal Post-Tensioned Concrete Box-Girder Road Bridges. Sustainability, 9(10), 1864. doi:10.3390/su9101864Navarro, I. J., Yepes, V., & Martí, J. V. (2018). Social life cycle assessment of concrete bridge decks exposed to aggressive environments. Environmental Impact Assessment Review, 72, 50-63. doi:10.1016/j.eiar.2018.05.003Sierra, L. A., Pellicer, E., & Yepes, V. (2017). Method for estimating the social sustainability of infrastructure projects. Environmental Impact Assessment Review, 65, 41-53. doi:10.1016/j.eiar.2017.02.004Navarro, I. J., Yepes, V., & Martí, J. V. (2019). Sustainability assessment of concrete bridge deck designs in coastal environments using neutrosophic criteria weights. Structure and Infrastructure Engineering, 16(7), 949-967. doi:10.1080/15732479.2019.1676791Tavana, M., Shaabani, A., Javier Santos-Arteaga, F., & Raeesi Vanani, I. (2020). A Review of Uncertain Decision-Making Methods in Energy Management Using Text Mining and Data Analytics. Energies, 13(15), 3947. doi:10.3390/en13153947Yannis, G., Kopsacheili, A., Dragomanovits, A., & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 413-431. doi:10.1016/j.jtte.2020.05.005Navarro, I. J., Penadés-Plà, V., Martínez-Muñoz, D., Rempling, R., & Yepes, V. (2020). LIFE CYCLE SUSTAINABILITY ASSESSMENT FOR MULTI-CRITERIA DECISION MAKING IN BRIDGE DESIGN: A REVIEW. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 26(7), 690-704. doi:10.3846/jcem.2020.13599Hedelin, B. (2018). Complexity is no excuse. Sustainability Science, 14(3), 733-749. doi:10.1007/s11625-018-0635-5Zadeh, L. A. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(1), 28-44. doi:10.1109/tsmc.1973.5408575Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xMilošević, D. M., Milošević, M. R., & Simjanović, D. J. (2020). Implementation of Adjusted Fuzzy AHP Method in the Assessment for Reuse of Industrial Buildings. Mathematics, 8(10), 1697. doi:10.3390/math8101697Lin, C.-N. (2020). A Fuzzy Analytic Hierarchy Process-Based Analysis of the Dynamic Sustainable Management Index in Leisure Agriculture. Sustainability, 12(13), 5395. doi:10.3390/su12135395Salehi, S., Jalili Ghazizadeh, M., Tabesh, M., Valadi, S., & Salamati Nia, S. P. (2020). A risk component-based model to determine pipes renewal strategies in water distribution networks. Structure and Infrastructure Engineering, 17(10), 1338-1359. doi:10.1080/15732479.2020.1842466Liu, P., & Liu, X. (2016). The neutrosophic number generalized weighted power averaging operator and its application in multiple attribute group decision making. International Journal of Machine Learning and Cybernetics, 9(2), 347-358. doi:10.1007/s13042-016-0508-0Peng, J., Wang, J., & Yang, W.-E. (2016). A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems. International Journal of Systems Science, 48(2), 425-435. doi:10.1080/00207721.2016.1218975Saaty, T. L., & Ozdemir, M. S. (2003). Why the magic number seven plus or minus two. Mathematical and Computer Modelling, 38(3-4), 233-244. doi:10.1016/s0895-7177(03)90083-5Harker, P. T. (1987). Incomplete pairwise comparisons in the analytic hierarchy process. Mathematical Modelling, 9(11), 837-848. doi:10.1016/0270-0255(87)90503-3Chen, K., Kou, G., Michael Tarn, J., & Song, Y. (2015). Bridging the gap between missing and inconsistent values in eliciting preference from pairwise comparison matrices. Annals of Operations Research, 235(1), 155-175. doi:10.1007/s10479-015-1997-zBozóki, S., Fülöp, J., & Rónyai, L. (2010). On optimal completion of incomplete pairwise comparison matrices. Mathematical and Computer Modelling, 52(1-2), 318-333. doi:10.1016/j.mcm.2010.02.047Dong, M., Li, S., & Zhang, H. (2015). Approaches to group decision making with incomplete information based on power geometric operators and triangular fuzzy AHP. Expert Systems with Applications, 42(21), 7846-7857. doi:10.1016/j.eswa.2015.06.007Zhou, X., Hu, Y., Deng, Y., Chan, F. T. S., & Ishizaka, A. (2018). A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP. Annals of Operations Research, 271(2), 1045-1066. doi:10.1007/s10479-018-2769-3Sumathi, I. R., & Antony Crispin Sweety, C. (2019). New approach on differential equation via trapezoidal neutrosophic number. Complex & Intelligent Systems, 5(4), 417-424. doi:10.1007/s40747-019-00117-3Deli, I., & Şubaş, Y. (2016). A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems. International Journal of Machine Learning and Cybernetics, 8(4), 1309-1322. doi:10.1007/s13042-016-0505-3Ye, J. (2017). Subtraction and Division Operations of Simplified Neutrosophic Sets. Information, 8(2), 51. doi:10.3390/info8020051Liang, R., Wang, J., & Zhang, H. (2017). A multi-criteria decision-making method based on single-valued trapezoidal neutrosophic preference relations with complete weight information. Neural Computing and Applications, 30(11), 3383-3398. doi:10.1007/s00521-017-2925-8Sodenkamp, M. A., Tavana, M., & Di Caprio, D. (2018). An aggregation method for solving group multi-criteria decision-making problems with single-valued neutrosophic sets. Applied Soft Computing, 71, 715-727. doi:10.1016/j.asoc.2018.07.020Sierra, L. A., Pellicer, E., & Yepes, V. (2016). Social Sustainability in the Lifecycle of Chilean Public Infrastructure. Journal of Construction Engineering and Management, 142(5), 05015020. doi:10.1061/(asce)co.1943-7862.0001099Abdel-Basset, M., Manogaran, G., Mohamed, M., & Chilamkurti, N. (2018). RETRACTED: Three-way decisions based on neutrosophic sets and AHP-QFD framework for supplier selection problem. Future Generation Computer Systems, 89, 19-30. doi:10.1016/j.future.2018.06.024Dubois, D. (2011). The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems, 184(1), 3-28. doi:10.1016/j.fss.2011.06.003Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. doi:10.1016/0165-0114(85)90090-9Wang, Y.-M., & Elhag, T. M. S. (2006). On the normalization of interval and fuzzy weights. Fuzzy Sets and Systems, 157(18), 2456-2471. doi:10.1016/j.fss.2006.06.008Enea, M., & Piazza, T. (2004). Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making, 3(1), 39-62. doi:10.1023/b:fodm.0000013071.63614.3dChu, T.-C., & Tsao, C.-T. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1-2), 111-117. doi:10.1016/s0898-1221(01)00277-

    Sustainability assessment of concrete bridge deck designs in coastal environments using neutrosophic criteria weights

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    "This is an Accepted Manuscript of an article published by Taylor & Francis in Structure and Infrastructure Engineering on 02/07/2020, available online: https://doi.org/10.1080/15732479.2019.1676791."[EN] Essential infrastructures such as bridges are designed to provide a long-lasting and intergenerational functionality. In those cases, sustainability becomes of paramount importance when the infrastructure is exposed to aggressive environments, which can jeopardise their durability and lead to significant maintenance demands. The assessment of sustainability is however often complex and uncertain. The present study assesses the sustainability performance of 16 alternative designs of a concrete bridge deck in a coastal environment on the basis of a neutrosophic group analytic hierarchy process (AHP). The use of neutrosophic logic in the field of multi-criteria decision-making, as a generalisation of the widely used fuzzy logic, allows for a proper capture of the vagueness and uncertainties of the judgements emitted by the decision-makers. TOPSIS technique is then used to aggregate the different sustainability criteria. From the results, it is derived that only the simultaneous consideration of the economic, environmental and social life cycle impacts of a design shall lead to adequate sustainable designs. Choices made based on the optimality of a design in only some of the sustainability pillars will lead to erroneous conclusions. The use of concrete with silica fume has resulted in a sustainability performance of 46.3% better than conventional concrete designs.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Navarro, I.; Yepes, V.; Martí, J. (2020). Sustainability assessment of concrete bridge deck designs in coastal environments using neutrosophic criteria weights. Structure and Infrastructure Engineering. 16(7):949-967. https://doi.org/10.1080/15732479.2019.1676791S949967167Abdel-Basset, M., Manogaran, G., Mohamed, M., & Chilamkurti, N. (2018). Three-way decisions based on neutrosophic sets and AHP-QFD framework for supplier selection problem. Future Generation Computer Systems, 89, 19-30. doi:10.1016/j.future.2018.06.024Abdullah, L., & Najib, L. (2014). Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia. International Journal of Sustainable Energy, 35(4), 360-377. doi:10.1080/14786451.2014.907292Ali, M. S., Aslam, M. S., & Mirza, M. S. (2015). A sustainability assessment framework for bridges – a case study: Victoria and Champlain Bridges, Montreal. Structure and Infrastructure Engineering, 1-14. doi:10.1080/15732479.2015.1120754Allacker, K. (2012). Environmental and economic optimisation of the floor on grade in residential buildings. The International Journal of Life Cycle Assessment, 17(6), 813-827. doi:10.1007/s11367-012-0402-2Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96. doi:10.1016/s0165-0114(86)80034-3Barone, G., & Frangopol, D. M. (2014). Life-cycle maintenance of deteriorating structures by multi-objective optimization involving reliability, risk, availability, hazard and cost. Structural Safety, 48, 40-50. doi:10.1016/j.strusafe.2014.02.002Biswas, P., Pramanik, S., & Giri, B. C. (2015). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications, 27(3), 727-737. doi:10.1007/s00521-015-1891-2Bolturk, E., & Kahraman, C. (2018). A novel interval-valued neutrosophic AHP with cosine similarity measure. Soft Computing, 22(15), 4941-4958. doi:10.1007/s00500-018-3140-yBuckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. doi:10.1016/0165-0114(85)90090-9Büyüközkan, G., & Göçer, F. (2017). Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Applied Soft Computing, 52, 1222-1238. doi:10.1016/j.asoc.2016.08.051Cebeci, U. (2009). Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert Systems with Applications, 36(5), 8900-8909. doi:10.1016/j.eswa.2008.11.046Chen, C., Habert, G., Bouzidi, Y., Jullien, A., & Ventura, A. (2010). LCA allocation procedure used as an incitative method for waste recycling: An application to mineral additions in concrete. Resources, Conservation and Recycling, 54(12), 1231-1240. doi:10.1016/j.resconrec.2010.04.001Chu, T.-C., & Tsao, C.-T. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1-2), 111-117. doi:10.1016/s0898-1221(01)00277-2Cope, A., Bai, Q., Samdariya, A., & Labi, S. (2013). Assessing the efficacy of stainless steel for bridge deck reinforcement under uncertainty using Monte Carlo simulation. Structure and Infrastructure Engineering, 9(7), 634-647. doi:10.1080/15732479.2011.602418De la Fuente, A., Pons, O., Josa, A., & Aguado, A. (2016). Multi-Criteria Decision Making in the sustainability assessment of sewerage pipe systems. Journal of Cleaner Production, 112, 4762-4770. doi:10.1016/j.jclepro.2015.07.002Deli, I., & Şubaş, Y. (2016). A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems. International Journal of Machine Learning and Cybernetics, 8(4), 1309-1322. doi:10.1007/s13042-016-0505-3Dong, Y., Zhang, G., Hong, W.-C., & Xu, Y. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49(3), 281-289. doi:10.1016/j.dss.2010.03.003Dubois, D. (2011). The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems, 184(1), 3-28. doi:10.1016/j.fss.2011.06.003Eamon, C. D., Jensen, E. A., Grace, N. F., & Shi, X. (2012). Life-Cycle Cost Analysis of Alternative Reinforcement Materials for Bridge Superstructures Considering Cost and Maintenance Uncertainties. Journal of Materials in Civil Engineering, 24(4), 373-380. doi:10.1061/(asce)mt.1943-5533.0000398Enea, M., & Piazza, T. (2004). Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making, 3(1), 39-62. doi:10.1023/b:fodm.0000013071.63614.3dGarcía-Segura, T., Penadés-Plà, V., & Yepes, V. (2018). Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty. Journal of Cleaner Production, 202, 904-915. doi:10.1016/j.jclepro.2018.08.177García-Segura, T., & Yepes, V. (2016). Multiobjective optimization of post-tensioned concrete box-girder road bridges considering cost, CO2 emissions, and safety. Engineering Structures, 125, 325-336. doi:10.1016/j.engstruct.2016.07.012García-Segura, T., Yepes, V., Frangopol, D. M., & Yang, D. Y. (2017). Lifetime reliability-based optimization of post-tensioned box-girder bridges. Engineering Structures, 145, 381-391. doi:10.1016/j.engstruct.2017.05.013Gervásio, H., & Simões da Silva, L. (2012). A probabilistic decision-making approach for the sustainable assessment of infrastructures. Expert Systems with Applications, 39(8), 7121-7131. doi:10.1016/j.eswa.2012.01.032Guzmán-Sánchez, S., Jato-Espino, D., Lombillo, I., & Diaz-Sarachaga, J. M. (2018). Assessment of the contributions of different flat roof types to achieving sustainable development. Building and Environment, 141, 182-192. doi:10.1016/j.buildenv.2018.05.063Heravi, G., Fathi, M., & Faeghi, S. (2017). Multi-criteria group decision-making method for optimal selection of sustainable industrial building options focused on petrochemical projects. Journal of Cleaner Production, 142, 2999-3013. doi:10.1016/j.jclepro.2016.10.168Invidiata, A., Lavagna, M., & Ghisi, E. (2018). Selecting design strategies using multi-criteria decision making to improve the sustainability of buildings. Building and Environment, 139, 58-68. doi:10.1016/j.buildenv.2018.04.041Jakiel, P., & Fabianowski, D. (2015). FAHP model used for assessment of highway RC bridge structural and technological arrangements. Expert Systems with Applications, 42(8), 4054-4061. doi:10.1016/j.eswa.2014.12.039Kahraman, C., Cebi, S., Onar, S. C., & Oztaysi, B. (2018). A novel trapezoidal intuitionistic fuzzy information axiom approach: An application to multicriteria landfill site selection. Engineering Applications of Artificial Intelligence, 67, 157-172. doi:10.1016/j.engappai.2017.09.009Kere, K. J., & Huang, Q. (2019). Life-Cycle Cost Comparison of Corrosion Management Strategies for Steel Bridges. Journal of Bridge Engineering, 24(4), 04019007. doi:10.1061/(asce)be.1943-5592.0001361Liang, R., Wang, J., & Zhang, H. (2017). A multi-criteria decision-making method based on single-valued trapezoidal neutrosophic preference relations with complete weight information. Neural Computing and Applications, 30(11), 3383-3398. doi:10.1007/s00521-017-2925-8Liu, P., & Liu, X. (2016). The neutrosophic number generalized weighted power averaging operator and its application in multiple attribute group decision making. International Journal of Machine Learning and Cybernetics, 9(2), 347-358. doi:10.1007/s13042-016-0508-0Martí, J. V., García-Segura, T., & Yepes, V. (2016). Structural design of precast-prestressed concrete U-beam road bridges based on embodied energy. Journal of Cleaner Production, 120, 231-240. doi:10.1016/j.jclepro.2016.02.024Martínez-Blanco, J., Lehmann, A., Muñoz, P., Antón, A., Traverso, M., Rieradevall, J., & Finkbeiner, M. (2014). Application challenges for the social Life Cycle Assessment of fertilizers within life cycle sustainability assessment. Journal of Cleaner Production, 69, 34-48. doi:10.1016/j.jclepro.2014.01.044Mistry, M., Koffler, C., & Wong, S. (2016). LCA and LCC of the world’s longest pier: a case study on nickel-containing stainless steel rebar. The International Journal of Life Cycle Assessment, 21(11), 1637-1644. doi:10.1007/s11367-016-1080-2Moazami, D., Behbahani, H., & Muniandy, R. (2011). Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic. Expert Systems with Applications, 38(10), 12869-12879. doi:10.1016/j.eswa.2011.04.079Mosalam, K. M., Alibrandi, U., Lee, H., & Armengou, J. (2018). Performance-based engineering and multi-criteria decision analysis for sustainable and resilient building design. Structural Safety, 74, 1-13. doi:10.1016/j.strusafe.2018.03.005Navarro, I., Yepes, V., & Martí, J. (2018). Life Cycle Cost Assessment of Preventive Strategies Applied to Prestressed Concrete Bridges Exposed to Chlorides. Sustainability, 10(3), 845. doi:10.3390/su10030845Navarro, I. J., Martí, J. V., & Yepes, V. (2019). Reliability-based maintenance optimization of corrosion preventive designs under a life cycle perspective. Environmental Impact Assessment Review, 74, 23-34. doi:10.1016/j.eiar.2018.10.001Navarro, I. J., Yepes, V., & Martí, J. V. (2018). Social life cycle assessment of concrete bridge decks exposed to aggressive environments. Environmental Impact Assessment Review, 72, 50-63. doi:10.1016/j.eiar.2018.05.003Navarro, I. J., Yepes, V., Martí, J. V., & González-Vidosa, F. (2018). Life cycle impact assessment of corrosion preventive designs applied to prestressed concrete bridge decks. Journal of Cleaner Production, 196, 698-713. doi:10.1016/j.jclepro.2018.06.110Nogueira, C. G., Leonel, E. D., & Coda, H. B. (2012). Reliability algorithms applied to reinforced concrete structures durability assessment. Revista IBRACON de Estruturas e Materiais, 5(4), 440-450. doi:10.1590/s1983-41952012000400003Pamučar, D., Badi, I., Sanja, K., & Obradović, R. (2018). A Novel Approach for the Selection of Power-Generation Technology Using a Linguistic Neutrosophic CODAS Method: A Case Study in Libya. Energies, 11(9), 2489. doi:10.3390/en11092489Penadés-Plà, V., García-Segura, T., Martí, J., & Yepes, V. (2016). A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design. Sustainability, 8(12), 1295. doi:10.3390/su8121295Peng, J., Wang, J., & Yang, W.-E. (2016). A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems. International Journal of Systems Science, 48(2), 425-435. doi:10.1080/00207721.2016.1218975Petcherdchoo, A. (2015). Environmental Impacts of Combined Repairs on Marine Concrete Structures. Journal of Advanced Concrete Technology, 13(3), 205-213. doi:10.3151/jact.13.205PRASCEVIC, N., & PRASCEVIC, Z. (2017). APPLICATION OF FUZZY AHP FOR RANKING AND SELECTION OF ALTERNATIVES IN CONSTRUCTION PROJECT MANAGEMENT. Journal of Civil Engineering and Management, 23(8), 1123-1135. doi:10.3846/13923730.2017.1388278Pryn, M. R., Cornet, Y., & Salling, K. B. (2015). APPLYING SUSTAINABILITY THEORY TO TRANSPORT INFRASTRUCTURE ASSESSMENT USING A MULTIPLICATIVE AHP DECISION SUPPORT MODEL. TRANSPORT, 30(3), 330-341. doi:10.3846/16484142.2015.1081281Rashidi, M., Samali, B., & Sharafi, P. (2015). A new model for bridge management: Part B: decision support system for remediation planning. Australian Journal of Civil Engineering, 14(1), 46-53. doi:10.1080/14488353.2015.1092642Sabatino, S., Frangopol, D. M., & Dong, Y. (2015). Life cycle utility-informed maintenance planning based on lifetime functions: optimum balancing of cost, failure consequences and performance benefit. Structure and Infrastructure Engineering, 12(7), 830-847. doi:10.1080/15732479.2015.1064968Safi, M., Sundquist, H., & Karoumi, R. (2015). Cost-Efficient Procurement of Bridge Infrastructures by Incorporating Life-Cycle Cost Analysis with Bridge Management Systems. Journal of Bridge Engineering, 20(6), 04014083. doi:10.1061/(asce)be.1943-5592.0000673Sajedi, S., & Huang, Q. (2019). Reliability-based life-cycle-cost comparison of different corrosion management strategies. Engineering Structures, 186, 52-63. doi:10.1016/j.engstruct.2019.02.018Sierra, L. A., Pellicer, E., & Yepes, V. (2016). Social Sustainability in the Lifecycle of Chilean Public Infrastructure. Journal of Construction Engineering and Management, 142(5), 05015020. doi:10.1061/(asce)co.1943-7862.0001099Sierra, L. A., Yepes, V., García-Segura, T., & Pellicer, E. (2018). Bayesian network method for decision-making about the social sustainability of infrastructure projects. Journal of Cleaner Production, 176, 521-534. doi:10.1016/j.jclepro.2017.12.140Sodenkamp, M. A., Tavana, M., & Di Caprio, D. (2018). An aggregation method for solving group multi-criteria decision-making problems with single-valued neutrosophic sets. Applied Soft Computing, 71, 715-727. doi:10.1016/j.asoc.2018.07.020Stewart, M. G., Estes, A. C., & Frangopol, D. M. (2004). Bridge Deck Replacement for Minimum Expected Cost Under Multiple Reliability Constraints. Journal of Structural Engineering, 130(9), 1414-1419. doi:10.1061/(asce)0733-9445(2004)130:9(1414)Swarr, T. E., Hunkeler, D., Klöpffer, W., Pesonen, H.-L., Ciroth, A., Brent, A. C., & Pagan, R. (2011). Environmental life-cycle costing: a code of practice. The International Journal of Life Cycle Assessment, 16(5), 389-391. doi:10.1007/s11367-011-0287-5Tahmasebi Birgani, Y., & Yazdandoost, F. (2018). An Integrated Framework to Evaluate Resilient-Sustainable Urban Drainage Management Plans Using a Combined-adaptive MCDM Technique. Water Resources Management, 32(8), 2817-2835. doi:10.1007/s11269-018-1960-2Tesfamariam, S., & Sadiq, R. (2006). Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP). Stochastic Environmental Research and Risk Assessment, 21(1), 35-50. doi:10.1007/s00477-006-0042-9Wang, Y.-M., & Elhag, T. M. S. (2006). On the normalization of interval and fuzzy weights. Fuzzy Sets and Systems, 157(18), 2456-2471. doi:10.1016/j.fss.2006.06.008Yang, Z., Shi, X., Creighton, A. T., & Peterson, M. M. (2009). Effect of styrene–butadiene rubber latex on the chloride permeability and microstructure of Portland cement mortar. Construction and Building Materials, 23(6), 2283-2290. doi:10.1016/j.conbuildmat.2008.11.011Ye, J. (2013). Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. International Journal of General Systems, 42(4), 386-394. doi:10.1080/03081079.2012.761609Ye, J. (2017). Subtraction and Division Operations of Simplified Neutrosophic Sets. Information, 8(2), 51. doi:10.3390/info8020051Yepes, V., García-Segura, T., & Moreno-Jiménez, J. M. (2015). A cognitive approach for the multi-objective optimization of RC structural problems. Archives of Civil and Mechanical Engineering, 15(4), 1024-1036. doi:10.1016/j.acme.2015.05.001Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZadeh, L. A. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(1), 28-44. doi:10.1109/tsmc.1973.5408575Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A., & Nor, K. M. (2016). Development of TOPSIS Method to Solve Complicated Decision-Making Problems — An Overview on Developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15(03), 645-682. doi:10.1142/s021962201630001
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