Disassembly　line　balancing　problem　(DLBP),　which　is　to　select　disassembly　process,　open　workstations　and　assign　selected　tasks　to　opened　workstations,　plays　an　important　role　in　the　recycling　of　End　Of　Life　products.　In　real-world　disassembly　operations,　task　processing　times　are　usually　stochastic　due　to　various　factors.　Most　related　works　address　the　uncertain　processing　times　by　assuming　that　the　probability　distribution　is　known　and　the　task　processing　times　are　independent　of　each　other.　In　practice,　however,　it　is　difficult　to　get　the　complete　distributional　information　and　there　is　always　underlying　correlation　between　the　uncertain　processing　times.　This　paper　investigates　the　DLBP　with　partial　uncertain　knowledge,　i.e.　the　mean　and　covariance　matrix　of　task　processing　times.　A　new　distributionally　robust　formulation　with　a　joint　chance　constraint　is　proposed.　To　solve　the　problem,　an　approximated　mixed　integer　second-order　cone　programming　(MI-SOCP)　model　is　proposed,　and　a　two-stage　parameter-adjusting　heuristic　is　further　developed.　Numerical　experiments　are　conducted,　to　evaluate　the　performance　of　the　proposed　method.　We　also　draw　some　managerial　insights　and　consider　an　extension　problem.