The　slacks-based　measure　(SBM)　and　super-efficiency　SBM　(S-SBM)　models　are　inoperable　when　inputs　or　outputs　have　zero　or　negative　data.　In　this　paper,　a　mixed-binary　linear　program　integrating　SBM　and　S-SBM　is　developed　to　handle　zero　and　negative　input　and　output　values.　The　model　is　units　invariant,　unlike　Tone　and　Chang　et　al.　(2020)＇s　BP-SBM　model.　The　model　also　addresses　the　limitations　of　modified　SBM　(MSBM)　(Sharp　et　al.,　2007)　by　dropping　the　requirement　for　minimum　inputs　and　maximum　outputs　and　avoids　the　sensitivity　of　the　models　using　a　range　adjusted　measure　(RAM)　to　the　range　of　the　maximum　and　minimum　values.　Three　sets　of　data　are　used　to　test　the　model　and　compare　it　with　BP-SBM　and　the　model　proposed　by　Lin　et　al.　(2019).　The　first　data　set　is　a　simple　illustration　to　simplify　understanding　of　the　data　translation　and　application　of　the　three　models.　The　second　data　set　comprises　30　Taiwanese　electrical　machinery　listed　firms　and　contains　negative　outputs.　The　third　data　set　is　the　energy　activities　of　30　Chinese　provinces　with　two　bad　outputs　that　are　treated　as　negative　data.　The　kernel　density　estimations　show　the　distributions　of　efficiency　scores　as　well　as　the　Simar-Zelenyuk　adapted　Li　tests　for　the　model　comparisons　of　the　bootstrapped　distributions　for　the　latter　two　data　sets.　The　results　of　these　data　sets　validate　the　usefulness　and　practicability　of　our　proposed　approach.　(c)　2022　Elsevier　Ltd.　All　rights　reserved.