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    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. 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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. 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Uncertain generalized aggregation operators. Expert Systems with Applications, 39(1), 1105-1117. doi:10.1016/j.eswa.2011.07.11

    Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators

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    [EN] There is a growing interest in environmental policies about how to implement public participation engagement in the context of water resources management. This paper presents a robust methodology, based on ordered weighted averaging (OWA) operators, to conflict resolution decision-making problems under uncertain environments due to both information and stakeholders' preferences. The methodology allows integrating heterogeneous interests of the general public and stakeholders on account of their different degree of acceptance or preference and level of influence or power regarding the measures and policies to be adopted, and also of their level of involvement (i.e., information supply, consultation and active involvement). These considerations lead to different environmental and socio-economic outcomes, and levels of stakeholders' satisfaction. The methodology establishes a prioritization relationship over the stakeholders. The individual stakeholders' preferences are aggregated through their associated weights, which depend on the satisfaction of the higher priority decision maker. The methodology ranks the optimal management strategies to maximize the stakeholders' satisfaction. It has been successfully applied to a real case study, providing greater fairness, transparency, social equity and consensus among actors. Furthermore, it provides support to environmental policies, such as the EU Water Framework Directive (WFD), improving integrated water management while covering a wide range of objectives, management alternatives and stakeholders.Llopis Albert, C.; Merigó-Lindahl, JM.; Liao, H.; Xu, Y.; Grima-Olmedo, J.; Grima-Olmedo, C. (2018). Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators. Water Resources Management. 32(2):497-510. https://doi.org/10.1007/s11269-017-1823-2S497510322Amin GR, Sadeghi H (2010) Application of prioritized aggregation operators in preference voting. Int J Intell Syst 25(10):1027–1034Chen TY (2014) A prioritized aggregation operator-based approach to multiple criteria decision making using interval-valued intuitionistic fuzzy sets: A comparative perspective. Inf Sci 281:97–112Chen LH, Xu ZS (2014) A prioritized aggregation operator based on the OWA operator and prioritized measures. J Intell Fuzzy Syst 27:1297–1307Chen LH, Xu ZS, Yu XH (2014a) Prioritized measure-guided aggregation operators. IEEE Trans Fuzzy Syst 22:1127–1138Chen LH, Xu ZS, Yu XH (2014b) Weakly prioritized measure aggregation in prioritized multicriteria decision making. Int J Intell Syst 29:439–461CHJ (2016). Júcar river basin authority http://www.chj.es/CHS (2016). Segura river basin authority http://www.chsegura.es/Dong JY, Wan SP (2016) A new method for prioritized multi-criteria group decision making with triangular intuitionistic fuzzy numbers. J Intell Fuzzy Syst 30:1719–1733EC (2000). Directive 2000/60/EC of the European Parliament and of the Council of October 23 2000 Establishing a Framework for Community Action in the Field of Water Policy. Official Journal of the European Communities, L327/1eL327/72 22.12.2000Jackson S, Tan P-L, Nolan S (2012) Tools to enhance public participation and confidence in the development of the Howard East aquifer water plan, Northern Territory. J Hydrol 474:22–28Jin FF, Ni ZW, Chen HY (2016) Note on “Hesitant fuzzy prioritized operators and their application to multiple attribute decision making”. Knowl-Based Syst 96:115–119Kentel E, Aral MM (2007) Fuzzy Multiobjective Decision-Making Approach for Groundwater Resources Management. J Hydrol Eng 12(2):206–217. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:2(206).Kirchherr J, Charles KJ, Walton MJ (2016) Multi-causal pathways of public opposition to dam project in Asia: A fuzzy set qualitative comparative analysis (fsQCA). Glob Environ Chang 41:33–45. https://doi.org/10.1016/j.gloenvcha.2016.08.001Llopis-Albert C, Pulido-Velazquez D (2015) Using MODFLOW code to approach transient hydraulic head with a sharp-interface solution. Hydrol Process 29(8):2052–2064. https://doi.org/10.1002/hyp.10354Llopis-Albert C, Palacios-Marqués D, Soto-Acosta P (2015) Decision-making and stakeholders constructive participation in environmental projects. J Bus Res 68:1641–1644. https://doi.org/10.1016/j.jbusres.2015.02.010Llopis-Albert C, Merigó JM, Xu Y, Huchang L (2017) Improving regional climate projections by prioritized aggregation via ordered weighted averaging operators. Environ Eng Sci. https://doi.org/10.1089/ees.2016.0546Maia R (2017) The WFD Implementation in the European Member States. Water Resour Manag 31(10):3043–3060. https://doi.org/10.1007/s11269-017-1723-5Malczewski J, Chapman T, Flegel C, Walters D, Shrubsole D, Healy MA (2003) GIS - multicriteria evaluation with ordered weighted averaging (OWA): case study of developing watershed management strategies. Environ Plan A 35:1769–1784. https://doi.org/10.1068/a35156Merigó JM, Casanovas M (2011) The uncertain generalized owa operator and its application to financial decision making. Int J Inf Technol Decis Mak 10(2):211–230Merigó JM, Yager RR (2013) Generalized moving averages, distance measures and OWA operators. Int J Uncertain, Fuzziness Knowl-Based Syst 21(4):533–559Merigó JM, Palacios-Marqués D, Ribeiro-Navarrete B (2015) Aggregation systems for sales forecasting. J Bus Res 68:2299–2304Mesiar R, Stupnanová A, Yager RR (2015) Generalizations of OWA Operators. IEEE Trans Fuzzy Syst 23(6):2154–2162O’Hagan M (1988) Aggregating Template Rule Antecedents in Real-time Expert Systems with Fuzzy Set Logic. In: Proceedings of 22nd annual IEEE Asilomar Conference on Signals. IEEE and Maple Press, Pacific Grove, Systems and Computers, pp 681–689Rahmani MA, Zarghami M (2013) A new approach to combine climate change projections by ordered weighting averaging operator; applications to northwestern provinces of Iran. Glob Planet Chang 102:41–50Ran LG, Wei GW (2015) Uncertain prioritized operators and their application to multiple attribute group decision making. Technol Econ Dev Econ 21:118–139Ruiz-Villaverde, A., García-Rubio, M.A. (2017). Public Participation in European Water Management: from Theory to Practice. Water Resour Manag 31(8), 2479–2495. https://doi.org/10.1007/s11269-016-1355-1Sadiq R, Tesfamariam S (2007) Probability density functions based weights for ordered weighted averaging (OWA) operators: An example of water quality indices. Eur J Oper Res 182:1350–1368Sadiq R, Rodríguez MJ, Tesfamariam S (2010) Integrating indicators for performance assessment of small water utilities using ordered weighted averaging (OWA) operators. Expert Syst Appl 37:4881–4891Verma R, Sharma B (2016) Prioritized information fusion method for triangular fuzzy information and its application to multiple attribute decision making. Int J Uncertain, Fuzziness Knowl-Based Syst 24:265–290Wang HM, Xu YJ, Merigó JM (2014) Prioritized aggregation for non-homogeneous group decision making in water resource management. Econ Comput Econ Cybern Stud Res 48(1):247–258Wei GW (2012) Hesitant fuzzy prioritized operators. Knowl-Based Syst 31:176–182Wei CP, Tang XJ (2012) Generalized prioritized aggregation operators. Int J Intell Syst 27:578–589Xu ZS (2005) An Overview of Methods for Determining OWA Weights. Int J Intell Syst 20:843–865Yager RR (1988) On ordered weighted averaging aggregation operators in multi-criteria decision making, IEEE Transactions on Systems. Man Cybern B 18(1988):183–190Yager RR (2008) Prioritized Aggregation Operators. Int J Approx Reason 48:263–274Yan H-B, Huynh V-N, Nakamori Y, Murai T (2011) On prioritized weighted aggregation in multi-criteria decision making. Expert Syst Appl 38(1):812–823Ye J (2014) Prioritized aggregation operators of trapezoidal intuitionistic fuzzy sets and their application to multicriteria decision-making. Neural Comput & Applic 25:1447–1454Yu XH, Xu ZS, Liu SS (2013) Prioritized multi-criteria decision making based on preference relations. Comput Ind Eng 66:104–115Zadeh LA (1983) A Computational Approach to Fuzzy Quantifiers in Natural Languages. Comput Math Appl 9:149–184Zarghami M, Szidarovszky F (2009) Revising the OWA operator for multi criteria decision making problems under uncertainty. 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Int J Uncertain, Fuzziness Knowl-Based Syst 21:603–627Zhou LG, Merigó JM, Chen HY, Liu JP (2016) The optimal group continuous logarithm compatibility measure for interval multiplicative preference relations based on the COWGA operator. Inf Sci 328:250–26

    Weighted Constraints in Fuzzy Optimization

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    Many practical optimization problems are characterized by someflexibility in the problem constraints, where this flexibility canbe exploited for additional trade-off between improving theobjective function and satisfying the constraints. Especially indecision making, this type of flexibility could lead to workablesolutions, where the goals and the constraints specified bydifferent parties involved in the decision making are traded offagainst one another and satisfied to various degrees. Fuzzy setshave proven to be a suitable representation for modeling this typeof soft constraints. Conventionally, the fuzzy optimizationproblem in such a setting is defined as the simultaneoussatisfaction of the constraints and the goals. No additionaldistinction is assumed to exist amongst the constraints and thegoals. This report proposes an extension of this model forsatisfying the problem constraints and the goals, where preferencefor different constraints and goals can be specified by thedecision-maker. The difference in the preference for theconstraints is represented by a set of associated weight factors,which influence the nature of trade-off between improving theoptimization objectives and satisfying various constraints.Simultaneous weighted satisfaction of various criteria is modeledby using the recently proposed weighted extensions of(Archimedean) fuzzy t-norms. The weighted satisfaction of theproblem constraints and goals are demonstrated by using a simplefuzzy linear programming problem. The framework, however, is moregeneral, and it can also be applied to fuzzy mathematicalprogramming problems and multi-objective fuzzy optimization.wiskundige programmering;fuzzy sets;optimalisatie

    Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

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    Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

    Decisión making with induced aggregation operators and the adequacy coefficient

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    We present a method for decision making by using induced aggregation operators. This method is very useful for business decision making problems such as product management, investment selection and strategic management. We introduce a new aggregation operator that uses the induced ordered weighted averaging (IOWA) operator and the weighted average in the adequacy coefficient. We callit the induced ordered weighted averaging weighted averaging adequacy coefficient (IOWAWAAC) operator. The main advantage is that it is able to deal with complex attitudinal characters in the aggregation process. Thus, we are able to give a better representation of the problem considering the complex environment that affects the decisions. Moreover, it is able to provide a unified framework between the OWA and the weighted average. We generalize it by using generalized aggregation operators, obtaining the induced generalized OWAWAAC (IGOWAWAAC) operator . We study some of the main properties of this approach. We end the paper with a numerical example of the new approach in a group decision making problem in strategic managemen
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