Non-radial　data　envelopment　analysis　(DEA)　models,　such　as　the　slacks-based　measure　(SBM)　model,　exert　an　important　role　in　theoretical　research　and　real　applications　on　efficiency　evaluation　and　improvement.　However,　those　models　maximize　input　and　output　slacks　and　may　therefore　find　a　rather　far　projection　for　each　inefficient　decision-making　unit　(DMU).　This　results　in　varying　performance　measures　and　inconveniences　in　improving　the　performance　of　inefficient　DMUs.　Tone　(2016)　proposed　a　new　non-oriented　SBM-Max　model　and　several　algorithms　to　overcome　such　limitations.　However,　those　algorithms　were　computationally　expensive　and　complicated,　limiting　their　applications　to　different　problems.　In　the　present　study,　algorithms　are　proposed　based　on　two　convex　hull　algorithms　to　make　a　novel　variation　of　(Tone,　2016)　that　is　easily　applicable　to　different　problems.　The　proposal　is　based　on　the　constant　returns-toscale　(CRS)　assumption.　The　algorithms　can　be　further　extended　based　on　other　assumptions　of　returns　to　scale.　The　used　two　convex　hull　algorithms　were　the　Quickhull　(Qhull)　algorithm　and　the　C++　(ANSI　C)　implementation　of　the　double　description　(CDD)　algorithm.　The　proposed　algorithms　were　tested　on　a　dataset　from　prior　literature　and　a　real　dataset　of　Hong　Kong　hospitals.　The　results　demonstrate　that　the　proposed　algorithms　are　effective　for　finding　a　close　projection　on　efficiency　evaluation,　resulting　in　improvements　in　DMUs.