With　green　and　smart　manufacturing　emergence　and　development,　effective　conduct　with　End　of　Life　(EOL)　products　is　an　extremely　important　and　hot　research　topic.　Disassembly　line　design　is　a　fundamental　and　complex　problem　in　EOL　product　recycling　and　remanufacturing,　especially　when　considering　uncertainty.　In　this　paper,　we　investigate　a　disassembly　line　balancing　problem　(DLBP)　with　stochastic　task　processing　time　to　minimize　the　cycle　time.　We　focus　on　selecting　alternative　task　processes,　determining　the　number　of　open　workstations　and　assigning　operational　tasks　to　the　workstations.　A　joint　chance-constrained　programming　is　proposed　and　we　adopt　a　distribution-free　method,　which　is　based　on　Markov　Inequality,　to　approximate　the　chance　constraints　as　the　distribution　is　difficult　to　fully　estimated.　Risk-neutral　model　may　provide　solutions　that　perform　poorly　while　considering　uncertainty.　Therefore,　a　risk-averse　measure　called　Conditional　Value-at-Risk　(CVaR),　which　is　rarely　used　in　the　DLBP,　is　considered　in　this　paper　to　incorporate　decision　makers＇　perspectives.　To　the　best　of　our　knowledge,　we　are　the　first　to　consider　risk-averse　model　in　DLBP　with　minimizing　cycle　time　under　uncertain　task　processing　time.　Finally,　computational　experiments　are　conducted　and　give　the　impacts　by　testing　different　risk　levels　and　confidence　levels.