
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
  <dc:title xml:lang="eng">Stepwise benchmarking for multiple criteria sorting</dc:title>
  <dc:format>application/pdf</dc:format>
  <dc:format>5597428 bytes</dc:format>
  <dc:rights>All rights reserved</dc:rights>
  <dc:identifier>https://phaidrabg.bg.ac.rs/o:29609</dc:identifier>
  <dc:identifier>doi:10.1016/j.omega.2021.102579</dc:identifier>
  <dc:identifier>ISSN: 0305-0483</dc:identifier>
  <dc:description xml:lang="eng">Abstract:
We propose a stepwise benchmarking framework for multiple criteria sorting, where alternatives are assigned to pre-defined and ordered decision classes. Our focus is on the alternatives currently attaining unfavorable classifications and aiming to reach a more preferred category through gradual performance modifications distributed over a few steps. We introduce strategies for the generation of development paths that incorporate either existing alternatives or fictive benchmarks. The latter ones are constructed using a suitably adapted framework for Post Factum Analysis. Its role is to highlight how the alternatives’ performances need to be modified minimally so that the desired sorting recommendation is attained. The proposed method is applicable in the context of classifications arrived with precise values of preference model parameters or multiple feasible parameter sets within the framework of robustness analysis. A Decision Maker is allowed to specify the constraints on the feasible performance improvements and define whether to build the development path based on all criteria or a subset of criteria. We propose the measures that capture the balance in modifications of performances on different criteria and various steps. They can evaluate all generated improvement plans and indicate the path providing the smoothest development toward more preferred classes by following the intermediate benchmarks. We also offer some supportive measures that quantify the contribution of different criteria in attaining the target assignment. The use of the proposed framework is illustrated in a real-world problem of parametric evaluation of research units. We analyze the outcomes derived with a dedicated outranking-based approach employed by the Polish Ministry of Science and Higher Education and discuss the development plans for some example units assigned to the least preferred class.</dc:description>
  <dc:description xml:lang="eng">Miłosz Kadziński acknowledges support from the Polish National Science Center under the SONATA BIS project (grant no. DEC-2019/34/E/HS4/00045). Mladen Stamenković acknowledges support from the Ministry of Education, Science and Technological Development of the Republic of Serbia. </dc:description>
  <dc:creator>Kadzinski, Milosz</dc:creator>
  <dc:creator id="https://orcid.org/0000-0002-3838-878X https://plus.cobiss.net/cobiss/sr/sr/conor/13835623">Stamenković, Mladen</dc:creator>
  <dc:creator>Uniejewski, Maciej</dc:creator>
  <dc:subject xml:lang="eng">Keywords: Development plan; Evolutionary multi-objective optimization; Multiple criteria sorting; Parametric evaluation; Performance modifications; Post factum analysis; Research unit; Stepwise benchmarking</dc:subject>
  <dc:language>eng</dc:language>
  <dc:date>2022</dc:date>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:source>Omega 108(April)</dc:source>
</oai_dc:dc>
