End-of-life　product　recycling　is　a　hot　research　topic　in　recent　years,　which　can　reduce　the　waste　and　protect　the　environment.　To　disassemble　products,　the　disassembly　line　balancing　is　a　principal　problem　that　selects　tasks　and　assigns　them　to　a　number　of　workstations　under　stochastic　task　processing　times.　In　existing　works,　stochastic　task　processing　times　are　usually　estimated　by　probability　distributions　or　fuzzy　numbers.　However,　in　real-life　applications,　only　their　partial　information　is　accessible.　This　paper　studies　a　bi-objective　stochastic　disassembly　line　balancing　problem　to　minimise　the　line　design　cost　and　the　cycle　time,　with　only　the　knowledge　of　the　mean,　standard　deviation　and　upper　bound　of　stochastic　task　processing　times.　For　the　problem,　a　bi-objective　chance-constrained　model　is　developed,　which　is　further　approximated　into　a　bi-objective　distribution-free　one.　Based　on　the　problem　analysis,　two　versions　of　the　epsilon-constraint　method　are　proposed　to　solve　the　transformed　model.　Finally,　a　fuzzy-logic　technique　is　adapted　to　propose　a　preferable　solution　for　decision　makers　according　to　their　preferences.　A　case　study　is　presented　to　illustrate　the　validity　of　the　proposed　models　and　algorithms.　Experimental　results　on　277　benchmark-based　and　randomly　generated　instances　show　the　efficiency　of　the　proposed　methods.