BeCoMe: Easy-to-implement optimized method for best-compromise group decision making: Flood-prevention and COVID-19 case studies
Tài liệu tham khảo
Bardossy, 1993, Combination of fuzzy numbers representing expert opinions, Fuzzy Set Syst., 57, 173, 10.1016/0165-0114(93)90157-D
Barreto-Neto, 2008, Application of fuzzy logic to the evaluation of runoff in a tropical watershed, Environ. Model. Software, 23, 244, 10.1016/j.envsoft.2007.07.006
Basili, 2020, Aggregation of experts’ opinions and conditional consensus opinion by the Steiner point, Int. J. Approx. Reason., 123, 17, 10.1016/j.ijar.2020.04.005
Coroianu, 2019, Piecewise linear approximation of fuzzy numbers: algorithms, arithmetic operations and stability of characteristics, Soft Computing, 23, 9491, 10.1007/s00500-019-03800-2
Donga, 2009, Linguistic multiperson decision making based on the use of multiple preference relations, Fuzzy Set Syst., 160, 603, 10.1016/j.fss.2008.08.011
Flaxman, 2020
Hannah, 2010, Advancing a research agenda for leadership in dangerous contexts, Mil. Psychol., 22, 157, 10.1080/08995601003644452
Kangas, 2005, Modelling ecological expertise for forest planning calculations-rationale, examples, and pitfalls, J. Environ. Manag., 76, 125, 10.1016/j.jenvman.2005.01.011
Klir, 1995
Kovar, 2002, Analysis of flood events on small river catchments using the KINFIL model, J. Hydrol. Hydromechanics, 50, 157
Kovář, 2018, How to reach a compromise solution on technical and non-structural flood control measures, Soil Water Res., 9, 143, 10.17221/27/2014-SWR
Kozierkiewicz-Hetmańska, 2017, The analysis of expert opinions' consensus quality, Inf. Fusion, 34, 80, 10.1016/j.inffus.2016.06.005
Krueger, 2012, A guide to expert opinion in environmental modeling and management, Environ. Model. Software, 36, 1, 10.1016/j.envsoft.2012.01.006
Krueger, 2012, The role of expert opinion in environmental modeling, Environ. Model. Software, 36, 4, 10.1016/j.envsoft.2012.01.011
Li, 2009, A multistage fuzzy stochastic programming model for supporting sustainable water-resources allocation and management, Environ. Model. Software, 24, 786, 10.1016/j.envsoft.2008.11.008
Lin, 2020, A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action, Int. J. Infect. Dis., 93, 211, 10.1016/j.ijid.2020.02.058
Luo, 2007, A spectrum of compromise aggregation operators for multi-attribute decision making, Artif. Intell., 171, 161, 10.1016/j.artint.2006.11.004
Mitroff, 2004
Nasiri, 2008, A fuzzy decision aid model for environmental performance assessment in waste recycling, Environ. Model. Software, 23, 677, 10.1016/j.envsoft.2007.04.009
Oliver, 2012, Valuing local knowledge as a source of expert data: farmer engagement and the design of decision support systems, Environ. Model. Software, 36, 76, 10.1016/j.envsoft.2011.09.013
Page, 2012, Eliciting fuzzy distributions from experts for ranking conceptual risk model components, Environ. Model. Software, 36, 19, 10.1016/j.envsoft.2011.03.001
Parchami
Paterson, 2008, A fuzzy decision support tool for wildlife translocations into communal conservancies in Namibia, Environ. Model. Software, 23, 521, 10.1016/j.envsoft.2007.07.005
Pearson, 1998, Reframing crisis management, Acad. Manag. Rev., 23, 59, 10.5465/amr.1998.192960
Pelikán, 2017, A generalized model for quantifying the impact of Ambient Intelligence on smart workplaces: applications in manufacturing, J. Ambient Intell. Smart Environ., 6, 651
Pelikán, 2017, Detection of resource overload in conditions of project ambiguity, IEEE Trans. Fuzzy Syst., 25, 868, 10.1109/TFUZZ.2016.2584645
Prem, 2020
Qu, 2019, Effective aggregation of expert opinions to inform environmental management: an integrated fuzzy group decision-making framework with application to cadmium-contaminated water treatment alternatives evaluation, J. Clean. Prod., 209, 834, 10.1016/j.jclepro.2018.10.277
Refsgaard, 2007, Uncertainty in the environmental modelling process – a framework and guidance, Environ. Model. Software, 22, 1543, 10.1016/j.envsoft.2007.02.004
Ritzema, 2010, Using participatory modelling to compensate for data scarcity in environmental planning: a case study from India, Environ. Model. Software, 25, 1450, 10.1016/j.envsoft.2010.03.010
Sanayei, 2010, Group decision making process for supplier selection with VIKOR under fuzzy environment, Expert Syst. Appl., 37, 24, 10.1016/j.eswa.2009.04.063
Service, 2020, Does disinfecting surfaces really prevent the spread of coronavirus? [archive] ; 12 March 2020, Science News/HealthCoronavirus
Tastle, 2007, Consensus and dissention. A measure of ordinal dispersion, International Journal of Approximating Reasoning, 45, 531, 10.1016/j.ijar.2006.06.024
Tastle, 2007, Determining risk assessment using the weighted ordinal agreement measure, J. Homel. Secur
Tichy, 2007, Making judgment calls, Harv. Bus. Rev., 85, 94
Tsabadze, 2006, A method for fuzzy aggregation based on group expert evaluations, Fuzzy Set Syst., 157, 1346, 10.1016/j.fss.2005.11.015
Vahdani, 2013, A new compromise solution method for fuzzy group decision-making problems with an application to the contractor selection, Eng. Appl. Artif. Intell., 26, 779, 10.1016/j.engappai.2012.11.005
Vaníček, 2009, Fuzzy aggregation and averaging for group decision making: a generalization and survey, Knowl. Base Syst., 22, 79, 10.1016/j.knosys.2008.07.002
Vrana, 2012, A group agreement-based approach for decision making in environmental issues, Environ. Model. Software, 36, 49, 10.1016/j.envsoft.2011.12.007
Vrana, 2012, Stakeholder group consensus based on multi-aspect hydrology decision making, J. Hydrol. Hydromechanics, 60, 252
Voinov, 2010, Modelling with stakeholders, Environ. Model. Software, 25, 1268, 10.1016/j.envsoft.2010.03.007
Weisstein, 2009
Woodward, 2003, Runoff curve number method: examination of the initial abstraction ratio, 1
Zhang, 2009, Fuzzy group decision-making for multi-format and multi-granularity linguistic judgments in quality function deployment, Expert Syst. Appl., 36, 9150, 10.1016/j.eswa.2008.12.027
Zimmermann, 1996