Recycling　of　end-of-life　(EOL)　products　has　drawn　much　attention　from　both　researchers　and　practitioners　over　the　recent　decades　due　to　the　environmental　protection,　sustainable　development　and　economic　benefits.　For　an　EOL　product　recycling　system,　a　core　problem　is　to　separate　their　useful　and　hazardous　parts　by　an　efficient　disassembly　line　in　which　there　exist　uncertain　factors,　such　as　stochastic　task　processing　time.　The　corresponding　combinatorial　optimization　problems　aim　to　optimally　choose　alternative　task　processes,　determine　the　number　of　workstations　to　be　opened,　and　assign　the　disassembly　tasks　to　the　opened　workstations.　In　most　existing　studies,　the　probability　distribution　of　task　processing　time　is　assumed　to　be　known.　However,　the　complete　information　of　probability　distribution　is　often　unavailable　due　to　various　factors.　In　this　study,　we　address　a　disassembly　line　balancing　problem　to　minimize　the　total　disassembly　cost　in　which　only　limited　information　of　probability　distribution,　i.e.,　the　mean,　lower　and　upper　bounds　of　task　processing　time,　is　known.　Based　on　problem　analysis,　some　properties　are　derived　for　the　construction　of　a　new　distribution-free　model.　Furthermore,　an　effective　second-order　cone　program　approximation-based　method　is　developed　to　solve　the　proposed　model.　Experimental　results　of　benchmark　examples　and　newly　generated　instances　demonstrate　the　effectiveness　and　efficiency　of　the　proposed　method　in　dealing　with　stochastic　disassembly　line　balancing　with　limited　distributional　information.　Finally,　managerial　insights　and　future　research　are　discussed.