Sorting　problem　has　become　one　of　the　most　common　Multi-Criteria　Decision-Making　(MCDM)　problems　in　real-life　scenarios.　In　classical　multi-criteria　sorting　methods,　the　input　data　are　required　to　be　precisely　expressed　by　quantitative　and　crisp　values.　However,　due　to　the　inherent　uncertainty　of　real-life　problems,　crisp　values　may　not　be　enough　to　model　the　decision　information.　In　addition,　while　multi-criteria　sorting　methods　often　utilize　group　intelligence,　they　seldom　address　the　challenges　of　Consensus　Reaching　Processes　(CRP)　as　non-cooperative　behaviors　or　human　bounded　rationality,　in　spite　of　they　are　common　challenges　in　MCDM.　In　such　a　context,　this　paper　develops　a　novel　multi-criteria　sorting　method　to　model　the　uncertainty,　manage　the　CRP　as　well　as　capture　the　human　bounded　rationality,　and　then　obtain　better　decision　solutions　by　considering　these　issues.　An　illustrative　numerical　example　is　presented　to　demonstrate　the　effectiveness　and　applicability　of　the　proposed　method.　The　results　highlight　the　potential　of　the　proposed　method　in　addressing　the　multi-criteria　sorting　problems　with　uncertain　and　imprecise　input　information　under　bounded　rationality　hypothesis.　Comparative　analysis　and　sensitivity　analysis　are　also　conducted　to　show　the　advantages　and　robustness　of　the　current　proposal,　respectively.　©　2025　Elsevier　B.V.