TOPSIS-RAD: Ranking According to Desires

Computer Science > Artificial Intelligence [Submitted on 5 Jun 2026] TOPSIS-RAD: Ranking According to Desires Leonardo Fernandes Costa, Helder Gomes Costa, Diogo Lima, Brunno Rodrigues Traditional TOPSIS derives its reference points -- the Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS) -- from the observed alternative set, making rankings susceptible to misalignment with decision-maker (DM) requirements, sensitivity to outlier performances, and rank reversal. This paper proposes TOPSIS-RAD, which addresses these issues by incorporating two arrays of DM-defined reference levels. Vetoed Performance Levels (VPL) exclude non-viable alternatives before normalisation, preventing them from distorting the ranking frontiers. Desired Performance Levels (DPL) cap performances at the DM's desired level before normalisation, anchoring the PIS in explicit aspirations rather than dataset extremes. Three toy examples demonstrate each mechanism: VPL reshapes normalisation boundaries by removing a non-viable alternative; fixed DPL frontiers stabilise rankings by limiting the influence of performances well above the desired level. The method preserves the familiar distance-based structure of TOPSIS while grounding the ranking in stable, DM-specified boundaries. Limitations and future research directions are also discussed. Comments: 21 pages, 15 Tables and 6 figures. The numerical computation of the data that appear in the Toy Examples was Supported by the Visual TOPSIS RAD that is available at this https URL. The data of the Toy examples are also available in this URL and can be loaded in the app as the template "Article" Subjects: Artificial Intelligence (cs.AI); Econometrics (econ.EM) Cite as: arXiv:2606.07253 [cs.AI]   (or arXiv:2606.07253v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.07253 Focus to learn more Submission history From: Helder Gomes Costa [view email] [v1] Fri, 5 Jun 2026 13:26:11 UTC (366 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs econ econ.EM References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)