MCDA Index Tool: an interactive software to develop indices and rankings

被引:60
作者
Cinelli M. [1 ,2 ]
Spada M. [3 ]
Kim W. [1 ]
Zhang Y. [1 ]
Burgherr P. [3 ]
机构
[1] Future Resilient Systems (FRS), Singapore-ETH Centre (SEC), Swiss Federal Institute of Technology (ETH) Zürich, Singapore
[2] Institute of Computing Science, Poznań University of Technology, Poznań
[3] Paul Scherrer Institut (PSI), Laboratory for Energy Systems Analysis, Villigen PSI
基金
欧盟地平线“2020”;
关键词
Aggregation; Composite indicator; Index development; MCDA; Normalization; Software;
D O I
10.1007/s10669-020-09784-x
中图分类号
学科分类号
摘要
A web-based software, called MCDA Index Tool (https://www.mcdaindex.net/), is presented in this paper. It allows developing indices and ranking alternatives, based on multiple combinations of normalization methods and aggregation functions. Given the steadily increasing importance of accounting for multiple preferences of the decision-makers and assessing the robustness of the decision recommendations, this tool is a timely instrument that can be used primarily by non-multiple criteria decision analysis (MCDA) experts to dynamically shape and evaluate their indices. The MCDA Index Tool allows the user to (i) input a dataset directly from spreadsheets with alternatives and indicators performance, (ii) build multiple indices by choosing several normalization methods and aggregation functions, and (iii) visualize and compare the indices’ scores and rankings to assess the robustness of the results. A novel perspective on uncertainty and sensitivity analysis of preference models offers operational solutions to assess the influence of different strategies to develop indices and visualize their results. A case study for the assessment of the energy security and sustainability implications of different global energy scenarios is used to illustrate the application of the MCDA Index Tool. Analysts have now access to an index development tool that supports constructive and dynamic evaluation of the stability of rankings driven by a single score while including multiple decision-makers’ and stakeholders’ preferences. © 2020, The Author(s).
引用
收藏
页码:82 / 109
页数:27
相关论文
共 91 条
  • [1] Alinezhad A., Khalili J., New methods and applications in multiple attribute decision making (MADM), (2019)
  • [2] Baizyldayeva U., Vlasov O., Kuandykov A.A., Akhmetov T.B., Multi-criteria decision support systems. Comparative analysis, Middle East J Sci Res, 16, pp. 1725-1730, (2013)
  • [3] Becker W., Saisana M., Paruolo P., Vandecasteele I., Weights and importance in composite indicators: closing the gap, Ecol Ind, 80, pp. 12-22, (2017)
  • [4] Bertin G., Carrino L., Giove S., The Italian regional well-being in a multi-expert non-additive perspective, Soc Indic Res, 135, pp. 15-51, (2018)
  • [5] Bisdorff R., Dias L., Mousseau V., Pirlot M., Meyer P., Evaluation and decision models with multiple criteria. Case Studies. International Handbooks on Information Systems, (2015)
  • [6] Blanco-Mesa F., Leon-Castro E., Merigo J.M., A bibliometric analysis of aggregation operators, Appl Soft Comput, 81, (2019)
  • [7] Bouyssou D., Jacquet-Lagreze E., Perny P., Slowinski R., Vanderpooten D., Vincke P., Aiding decisions with multiple criteria. Essays in Honor of Bernard Roy, (2002)
  • [8] Bouyssou D., Marchant T., Pirlot M., Tsoukias A., Vincke P., Problem formulation and structuring: the decision aiding process, Evaluation and decision models with multiple criteria: stepping stones for the analyst, pp. 19-65, (2006)
  • [9] Bouyssou D., Marchant T., Pirlot M., Tsoukias A., Vincke P., Building recommendations, Evaluation and decision models with multiple criteria: case studies, pp. 89-113, (2015)
  • [10] Burgass M.J., Halpern B.S., Nicholson E., Milner-Gulland E.J., Navigating uncertainty in environmental composite indicators, Ecol Ind, 75, pp. 268-278, (2017)