The　aim　of　this　paper　is　to　develop　a　closeness　coefficient　based　nonlinear　programming　method　for　solving　multiattribute　decision　making　problems　in　which　ratings　of　alternatives　on　attributes　are　expressed　using　interval-valued　intuitionistic　fuzzy　(IVIF)　sets　and　preference　information　on　attributes　is　incomplete.　In　this　methodology,　nonlinear　programming　models　are　constructed　on　the　concept　of　the　closeness　coefficient,　which　is　defined　as　a　ratio　of　the　square　of　the　weighted　Euclidean　distance　between　an　alternative　and　the　IVIF　negative　ideal　solution　(IVIFNIS)　to　the　sum　of　the　squares　of　the　weighted　Euclidean　distances　between　the　alternative　and　the　IVIF　positive　ideal　solution　(IVIFPIS)　as　well　as　the　IVIFNIS.　Simpler　nonlinear　programming　models　are　deduced　to　calculate　closeness　intuitionistic　fuzzy　sets　of　alternatives　to　the　IVIFPIS,　which　are　used　to　estimate　the　optimal　degrees　of　membership　and　hereby　generate　ranking　order　of　the　alternatives.　The　derived　auxiliary　nonlinear　programming　models　are　shown　to　be　flexible　with　different　information　structures　and　decision　environments.　The　proposed　method　is　validated　and　compared　with　other　methods.　A　real　example　is　examined　to　demonstrate　applicability　of　the　proposed　method　in　this　paper.　©　2011　Elsevier　B.V.