Fuzzy　TODIM　method　has　been　widely　and　successfully　used　to　solve　different　decision　making　problems　and　consider　decision　maker＇s　(DM＇s)　psychological　behavior　during　the　decision　process　under　uncertain　environment.　In　the　existing　fuzzy　TODIM　methods,　the　fuzzy　preferences　are　defuzzified　into　crisp　values　by　using　a　distance　measure　between　fuzzy　numbers　or　variables.　However,　such　an　operation　may　imply　a　significant　loss　of　information.　It　is　argued　that　if　fuzzy　information　is　defuzzified　into　crisp　values　at　the　very　beginning,　then　the　superiority　of　considering　fuzzy　information　is　discounted.　Therefore,　it　seems　better　to　keep　as　much　information　as　possible　during　the　decision　process　rather　than　oversimplifying　the　fuzzy　information　by　crisp　values.　An　effective　and　proper　way　for　keeping　as　much　information　as　possible　dealing　with　fuzzy　preferences　during　the　decision　process　is　the　use　of　alpha　level　sets.　Several　fuzzy　multi-criteria　decision　making　(MCDM)　methods　based　on　alpha　level　sets　have　been　proposed　and　used　to　handle　fuzzy　information　successfully,　however,　they　neglected　DM＇s　psychological　behavior　that　plays　a　critical　role　in　the　real　world　decision　processes.　Up　to　now,　there　is　not　a　fuzzy　MCDM　method　employing　alpha　level　sets　to　deal　with　fuzzy　information　together　with　considering　DM＇s　psychological　behavior.　Motivated　by　previous　limitations,　this　study　proposes　a　novel　fuzzy　TODIM　method　based　on　alpha　level　sets,　which　keeps　the　fuzzy　information　longer　and　considers　DM＇s　psychological　behavior　in　the　decision　process.　In　addition,　different　ways　to　select　the　best　alternative　are　provided　in　the　proposed　method.　Comparisons　with　several　MCDM　methods　are　presented　to　show　the　improvements　both　of　dealing　with　alpha　level　sets　when　psychological　behavior　is　considered　and　the　sensitivity　of　using　such　behavior　regarding　those　MCDM　methods　that　do　not　consider　it.　Through　the　comparison　analysis,　the　proposed　method　is　significant　superiority　to　the　existing　approaches,　which　not　only　improves　the　current　studies,　but　also　enriches　the　way　of　coping　with　fuzzy　information　in　the　extant　fuzzy　MCDM　methods.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.