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arXiv AI By Alexandre Flachs, Yves De Smet

From Outranking Flow Scores to Linear Programs: Group Decision Making in PROMETHEE II

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The paper presents a linear reparametrization of PROMETHEE II net flow scores that incorporates both criteria weights and preference functions, enabling a clearer understanding of their interaction. Using this representation, the authors formulate linear optimization models for three inverse‑analysis scenarios: (1) minimizing weight deviations while allowing preference functions to vary, (2) minimizing deviations over all preference parameters, and (3) achieving consensus among multiple decision makers with objectives based on total or maximal individual deviation to promote fairness. An illustrative example demonstrates that permitting preference functions to vary can substantially alter inverse‑analysis conclusions compared to weight‑only models, and that the group formulations can distinguish efficient consensuses from fairer ones.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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