237 research outputs found

    Group-decision making with induced ordered weighted logarithmic aggregation operators

    Get PDF
    This paper presents the induced generalized ordered weighted logarithmic aggregation (IGOWLA) operator, this operator is an extension of the generalized ordered weighted logarithmic aggregation (GOWLA) operator. It uses order-induced variables that modify the reordering process of the arguments included in the aggregation. The principal advantage of the introduced induced mechanism is the consideration of highly complex attitude from the decision makers. We study some families of the IGOWLA operator as measures for the characterization of the weighting vector (...

    Government transparency measurement through prioritized distance operators

    Full text link
    © 2018 - IOS Press and the authors. All rights reserved. The prioritized induced probabilistic ordered weighted average distance (PIPOWAD) has been developed. This new operator is an extension of the ordered weighted average (OWA) operator that can be used in cases where we have two sets of data that want to be compared. Some of the main characteristics of this new operator are: 1) Not all the decision makers are equally important, so the information needs to be prioritized, 2) The information has a probability to occur and 3) The decision makers can change the importance of the information based in an induced variable. Additionally, characteristics and families of the PIPOWAD operator are presented. Finally, an application of the PIPOWAD operator in order to measure government transparency in Mexico is presented

    Induced and logarithmic distances with multi-region aggregation operators

    Get PDF
    Copyright © 2019 The Author(s). Published by VGTU Press. This paper introduces the induced ordered weighted logarithmic averaging IOWLAD and multiregion induced ordered weighted logarithmic averaging MR-IOWLAD operators. The distinctive characteristic of these operators lies in the notion of distance measures combined with the complex reordering mechanism of inducing variables and the properties of the logarithmic averaging operators. The main advantage of MR-IOWLAD operators is their design, which is specifically thought to aid in decision-making when a set of diverse regions with different properties must be considered. Moreover, the induced weighting vector and the distance measure mechanisms of the operator allow for the wider modeling of problems, including heterogeneous information and the complex attitudinal character of experts, when aiming for an ideal scenario. Along with analyzing the main properties of the IOWLAD operators, their families and specific cases, we also introduce some extensions, such as the induced generalized ordered weighted averaging IGOWLAD operator and Choquet integrals. We present the induced Choquet logarithmic distance averaging ICLD operator and the generalized induced Choquet logarithmic distance averaging IGCLD operator. Finally, an illustrative example is proposed, including real-world information retrieved from the United Nations World Statistics for global regions

    Fuzzy decision making in complex frameworks with generalized aggregation operators

    Get PDF
    [EN] This article presents a new aggregation system applied to fuzzy decision making. The fuzzy generalized unified aggregation operator (FGUAO) is a system that integrates many operators by adding a new aggregation process that considers the relevance that each operator has in the analysis. It also deals with an uncertain environment where the information is studied with fuzzy numbers. A wide range of particular cases and properties are studied. This approach is further extended by using quasi-arithmetic means. The paper ends studying the applicability in decision making problems regarding the European Union decisions. For doing so, the work uses a multi-person aggregation process obtaining the multi-person - FGUAO operator. An example concerning the fixation of the interest rate by the European Central Bank is presented. (C) 2018 Elsevier B.V. All rights reserved.We would like to thank the associate editor and the anonymous reviewers for valuable comments that have improved the quality of the paper. Support from the Chilean Government through the Fondecyt Regular program (project number 1160286), the University of Chile, the project PIEF-GA-2011-300062 of the European Commission and the Distinguished Scientist Fellowship Program of the King Saud University (Saudi Arabia), is gratefully acknowledged.Merigó -Lindahl, JM.; Gil-Lafuente, AM.; Yu, D.; Llopis Albert, C. (2018). Fuzzy decision making in complex frameworks with generalized aggregation operators. Applied Soft Computing. 68:314-321. https://doi.org/10.1016/j.asoc.2018.04.002S3143216

    OWA operators in the calculation of the average green-house gases emissions

    Get PDF
    This study proposes, through weighted averages and ordered weighted averaging operators, a new aggregation system for the investigation of average gases emissions. We present the ordered weighted averaging operators gases emissions, the induced ordered weighted averaging operators gases emissions, the weighted ordered weighted averaging operators gases emissions and the induced probabilistic weighted ordered weighted averaging operators gases emissions. These operators represent a new way of analyzing the average gases emissions of different variables like countries or regions. The work presents further generalizations by using generalized and quasi-arithmetic means. The article also presents an illustrative example with respect to the calculations of the average gases emissions in the European region

    Decision making in reinsurance with induced OWA operators and Minkowski distances

    Get PDF
    The decision to choose a reinsurance program has many complexities because it is difficult to simultaneously achieve high levels in different optimal criteria including maximum gain, minimum variance, and probability of ruin. This article suggests a new method by which, through membership functions, we can measure the distance of each alternative to an optimal result and aggregate it by using different types of aggregations. In this article, particular attention is given to the induced Minkowski ordered weighted averaging distance operator and the induced Minkowski probabilistic ordered weighted averaging distance operator. The main advantage of these operators is that they include a wide range of special cases. Thus, they can adapt efficiently to the specific needs of the calculation processes. By doing so, the reinsurance system can make better decisions by using different scenarios in the uncertain environment considered

    Using Ordered Weighted Average for Weighted Averages Inflation

    Get PDF
    This paper presents the ordered weighted average weighted average inflation (OWAWAI) and some extensions using induced and heavy aggregation operators and presents the generalized operators and some of their families. The main advantage of these new formulations is that they can use two different sets of weighting vectors and generate new scenarios based on the reordering of the arguments with the weights. With this idea, it is possible to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision-maker. The work presents an application of these new approaches in the analysis of the inflation in Chile, Colombia, and Argentina during 2017

    Heavy moving averages and their application in econometric forecasting

    Get PDF
    This paper presents the heavy ordered weighted moving average (HOWMA) operator. It is an aggregation operator that uses the main characteristics of two well-known techniques: the heavy ordered weighted averaging (OWA) and the moving averages. Therefore, this operator provides a parameterized family of aggregation operators from the minimum to the total operator and includes the OWA operator as a special case. It uses a heavy weighting vector in the moving average formulation and it represents the information available and the knowledge of the decision maker about the future scenarios of the phenomenon, according to his attitudinal character. Some of the main properties of this operator are studied, including a wide range of families of HOWMA operators such as the heavy moving average and heavy weighted moving average operators. The HOWMA operator is also extended using generalized and quasi-arithmetic means. An example concerning the foreign exchange rate between US dollars and Mexican pesos is also presented

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

    Full text link
    [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. Eur J Oper Res 198:259–265Zarghami M, Ardakanian R, Memariani A, Szidarovszky F (2008) Extended OWA Operator for Group Decision Making on Water Resources Projects. J Water Resour Plan Manag 134(3):266–275. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:3(266)Zarghami M, Szidarovszky F, Ardakanian R (2009) Multi-attribute decision making on inter-basin water transfer projects. Transaction E. Ind Eng 16(1):73–80Zhao XF, Li QX, Wei GW (2014) Some prioritized aggregating operators with linguistic information and their application to multiple attribute group decision making. J Intell Fuzzy Syst 26:1619–1630Zhao N, Xu ZS, Ren ZL (2016) On typical hesitant fuzzy prioritized “or” operator in multi-attribute decision making. Int J Intell Syst 31:73–100Zhou LY, Lin R, Zhao XF, Wei GW (2013) Uncertain linguistic prioritized aggregation operators and their application to multiple attribute group decision making. 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

    A new measure of volatility using induced heavy moving averages

    Get PDF
    The volatility is a dispersion technique widely used in statistics and economics. This paper presents a new way to calculate volatility by using different extensions of the ordered weighted average (OWA) operator. This approach is called the induced heavy ordered weighted moving average (IHOWMA) volatility. The main advantage of this operator is that the classical volatility formula only takes into account the standard deviation and the average, while with this formulation it is possible to aggregate information according to the decision maker knowledge, expectations and attitude about the future. Some particular cases are also presented when the aggregation information process is applied only on the standard deviation or on the average. An example in three different exchange rates for 2016 are presented, these are for: USD/MXN, EUR/MXN and EUR/USD
    corecore