OWA operators in human resource management

We develop a new approach that uses the ordered weighted averaging (OWA) operator in different methods for the selection of human resources. The objective of this new model is to manipulate the neutrality of the old methods, so the decision maker can select human resources according to his degree of optimism or pessimism. In order to develop this model, first, a short revision of the OWA operators is introduced. Next, we briefly explain the general model for the selection of human resources and suggest three new indexes for the selection of human resources that use the OWA operator and the hybrid average in the Hamming distance, in the adequacy coefficient and in the index of maximum and minimum level. The main advantage of this method is that it is more complete than the previous ones so the decision maker gets a better understanding of the decision problem. The work ends with an illustrative example that shows the results obtained by using different types of aggregation operators in the new approaches.

Government transparency measurement through prioritized distance operators

© 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

The ordered weighted average inflation

This paper introduces the ordered weighted average inflation (OWAI). The OWAI operator aggregates the information of a set of inflations and provides a range of scenarios from the minimum and the maximum inflation. The advantage of this approach is that it can provide a flexible inflation formula that can be adapted to the specific characteristics of the enterprise, region, state or country (...

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

[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

Applications of ordered weighted averaging (OWA) operators in environmental problems

[EN] This paper presents an application of a prioritized weighted aggregation operator based on ordered weighted averaging (OWA) to deal with stakeholders' constructive participation in water resources projects. They have different degree of acceptance or preference regarding the measures and policies to be carried out, which lead to different environmental and socio-economic outcomes, and hence, to different levels of stakeholders’ satisfaction. The methodology establishes a prioritization relationship upon the stakeholders, which preferences are aggregated by means of weights depending on the satisfaction of the higher priority policy maker. The methodology establishes a prioritization relationship upon the stakeholders, which preferences are aggregated by means of weights depending on the satisfaction of the higher priority policy maker. The methodology has been successfully applied to a Public Participation Project (PPP) in watershed management, thus obtaining efficient environmental measures in conflict resolution problems under actors’ preference uncertainties. Guardar / Salir Siguiente >Llopis Albert, C.; Palacios Marqués, D. (2017). Applications of ordered weighted averaging (OWA) operators in environmental problems. Multidisciplinary Journal for Education, Social and Technological Sciences. 4(1):52-63. doi:10.4995/muse.2017.7001SWORD526341Berbegal-Mirabent, J., & Llopis-Albert, C. (2016). Applications of fuzzy logic for determining the driving forces in collaborative research contracts. Journal of Business Research, 69(4), 1446-1451. doi:10.1016/j.jbusres.2015.10.123Llopis-Albert, C., Merigó, J. M., & Xu, Y. (2016). A coupled stochastic inverse/sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty. Journal of Hydrology, 540, 774-783. doi:10.1016/j.jhydrol.2016.06.065Llopis-Albert, C., & Palacios-Marques, D. (2016). Applied Mathematical Problems in Engineering. Multidisciplinary Journal for Education, Social and Technological Sciences, 3(2), 1. doi:10.4995/muse.2016.6679Llopis-Albert, C., Palacios-Marques, D., & Soto-Acosta, P. (2015). Decision-making and stakeholders’ constructive participation in environmental projects. Journal of Business Research, 68(7), 1641-1644. doi:10.1016/j.jbusres.2015.02.010Llopis-Albert, C., & Pulido-Velazquez, D. (2014). Using MODFLOW code to approach transient hydraulic head with a sharp-interface solution. Hydrological Processes, 29(8), 2052-2064. doi:10.1002/hyp.10354Llopis-Albert, C., Merigó, J. M., & Palacios-Marqués, D. (2014). Structure Adaptation in Stochastic Inverse Methods for Integrating Information. Water Resources Management, 29(1), 95-107. doi:10.1007/s11269-014-0829-2Llopis-Albert, C., & Pulido-Velazquez, D. (2013). Discussion about the validity of sharp-interface models to deal with seawater intrusion in coastal aquifers. Hydrological Processes, 28(10), 3642-3654. doi:10.1002/hyp.9908Llopis-Albert, C., Palacios-Marqués, D., & Merigó, J. M. (2014). A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty. Journal of Hydrology, 511, 10-16. doi:10.1016/j.jhydrol.2014.01.021Llopis-Albert, C., & Capilla, J. E. (2010). Stochastic Simulation of Non-Gaussian 3D Conductivity Fields in a Fractured Medium with Multiple Statistical Populations: Case Study. Journal of Hydrologic Engineering, 15(7), 554-566. doi:10.1061/(asce)he.1943-5584.0000214Llopis-Albert, C., & Capilla, J. E. (2010). Stochastic inverse modelling of hydraulic conductivity fields taking into account independent stochastic structures: A 3D case study. Journal of Hydrology, 391(3-4), 277-288. doi:10.1016/j.jhydrol.2010.07.028Molina, J.-L., Pulido-Velázquez, M., Llopis-Albert, C., & Peña-Haro, S. (2013). Stochastic hydro-economic model for groundwater quality management using Bayesian networks. Water Science and Technology, 67(3), 579-586. doi:10.2166/wst.2012.598O’Hagan, M. (s. f.). Aggregating Template Or Rule Antecedents In Real-time Expert Systems With Fuzzy Set Logic. Twenty-Second Asilomar Conference on Signals, Systems and Computers. doi:10.1109/acssc.1988.754637Peña-Haro, S., Llopis-Albert, C., Pulido-Velazquez, M., & Pulido-Velazquez, D. (2010). Fertilizer standards for controlling groundwater nitrate pollution from agriculture: El Salobral-Los Llanos case study, Spain. Journal of Hydrology, 392(3-4), 174-187. doi:10.1016/j.jhydrol.2010.08.006Peña-Haro, S., Pulido-Velazquez, M., & Llopis-Albert, C. (2011). Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty. Environmental Modelling & Software, 26(8), 999-1008. doi:10.1016/j.envsoft.2011.02.010Pulido-Velazquez, D., Llopis-Albert, C., Peña-Haro, S., & Pulido-Velazquez, M. (2011). Efficient conceptual model for simulating the effect of aquifer heterogeneity on natural groundwater discharge to rivers. Advances in Water Resources, 34(11), 1377-1389. doi:10.1016/j.advwatres.2011.07.010Verma, R., & Sharma, B. (2016). Prioritized Information Fusion Method for Triangular Fuzzy Information and Its Application to Multiple Attribute Decision Making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 24(02), 265-289. doi:10.1142/s0218488516500136Wang, H.M., Xu, Y.J., Merigó, J.M., (2014). Prioritized aggregation for nonhomogeneous group decision making in water resource management. Economic Computation and Economic Cybernetics Studies and Research, 48(1), 247-258.Xu, Z. (2005). An overview of methods for determining OWA weights. International Journal of Intelligent Systems, 20(8), 843-865. doi:10.1002/int.20097Xu, Y., Llopis-Albert, C., & González, J. (2014). An application of structural equation modeling for continuous improvement. Computer Science and Information Systems, 11(2), 797-808. doi:10.2298/csis130113043xYager, 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. (2004). Modeling Prioritized Multicriteria Decision Making. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34(6), 2396-2404. doi:10.1109/tsmcb.2004.837348Yager, R. R. (2008). Prioritized aggregation operators. International Journal of Approximate Reasoning, 48(1), 263-274. doi:10.1016/j.ijar.2007.08.009Yager, R. R. (2009). Prioritized OWA aggregation. Fuzzy Optimization and Decision Making, 8(3), 245-262. doi:10.1007/s10700-009-9063-4Yager, 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:10.1007/978-3-642-17910-5Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications, 9(1), 149-184. doi:10.1016/0898-1221(83)90013-

Aggregation operators in group decision making: Identifying citation classics via H-classics

To analyze the past, present and future of a particular research field, classic papers are usually studied because they identify the highly cited papers being a relevant reference point in that specific research area. As a result of the possible mapping between high quality research and high citation counts, highly cited papers are very interesting. The objective of this study is to use the H-classics method, which is based on the popular h-index, to identify and analyze the highly cited documents published about aggregation operators in the research area of group decision making. According to the H-classics method, this research area is represented by 87 citation classics, which have been published from 1988 to 2014. Authors, affiliations (universities/institutions and countries), journals, books and conferences, and the topics covered by these 87 highly cited papers are studied.The authors would like to thank FEDER financial support from the Projects TIN2013-40658-P and TIN2016- 75850-P

A Semidefinite Programming approach for minimizing ordered weighted averages of rational functions

This paper considers the problem of minimizing the ordered weighted average (or ordered median) function of finitely many rational functions over compact semi-algebraic sets. Ordered weighted averages of rational functions are not, in general, neither rational functions nor the supremum of rational functions so that current results available for the minimization of rational functions cannot be applied to handle these problems. We prove that the problem can be transformed into a new problem embedded in a higher dimension space where it admits a convenient representation. This reformulation admits a hierarchy of SDP relaxations that approximates, up to any degree of accuracy, the optimal value of those problems. We apply this general framework to a broad family of continuous location problems showing that some difficult problems (convex and non-convex) that up to date could only be solved on the plane and with Euclidean distance, can be reasonably solved with different $\ell_p$-norms and in any finite dimension space. We illustrate this methodology with some extensive computational results on location problems in the plane and the 3-dimension space.Comment: 27 pages, 1 figure, 7 table

Dual hesitant fuzzy aggregation operators

Dual hesitant fuzzy sets (DHFSs) is a generalization of fuzzy sets (FSs) and it is typical of membership and non-membership degrees described by some discrete numerical. In this article we chiefly concerned with introducing the aggregation operators for aggregating dual hesitant fuzzy elements (DHFEs), including the dual hesitant fuzzy arithmetic mean and geometric mean. We laid emphasis on discussion of properties of newly introduced operators, and give a numerical example to describe the function of them. Finally, we used the proposed operators to select human resources outsourcing suppliers in a dual hesitant fuzzy environment. First published online: 11 Sep 201

Uncertain prioritized operators and their application to multiple attribute group decision making

In this paper, we investigate the uncertain multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Motivated by the idea of prioritized aggregation operators (Yager 2008), we develop some prioritized aggregation operators for aggregating uncertain information, and then apply them to develop some models for uncertain multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness

Approaches to multi-attribute group decision-making based on picture fuzzy prioritized Aczel–Alsina aggregation information

The Aczel-Alsina t-norm and t-conorm were derived by Aczel and Alsina in 1982. They are modified forms of the algebraic t-norm and t-conorm. Furthermore, the theory of picture fuzzy values is a very valuable and appropriate technique for describing awkward and unreliable information in a real-life scenario. In this research, we analyze the theory of averaging and geometric aggregation operators (AOs) in the presence of the Aczel-Alsina operational laws and prioritization degree based on picture fuzzy (PF) information, such as the prioritized PF Aczel-Alsina average operator and prioritized PF Aczel-Alsina geometric operator. Moreover, we examine properties such as idempotency, monotonicity and boundedness for the derived operators and also evaluated some important results. Furthermore, we use the derived operators to create a system for controlling the multi-attribute decision-making problem using PF information. To show the approach's effectiveness and the developed operators' validity, a numerical example is given. Also, a comparative analysis is presented