As　an　important　part　in　product　recycling,　disassembly　line　balancing　problem　(DLBP)　has　attracted　a　large　amount　of　attention.　Stochastic　DLBP　is　now　a　hot　and　challenging　research　topic,　due　to　its　wide　applications.　This　work　investigates　a　DLBP　with　uncertain　task　times,　where　the　distributional　information　is　limited,　i.e.　only　the　mean　values　and　standard　deviations　are　given,　due　to　the　lack　of　data.　From　a　two-stage　perspective,　a　disassembly　process　is　determined　and　the　disassembly　tasks　are　assigned　to　workstations　in　the　first　stage,　and　the　penalty　cost　(i.e.　the　recourse　cost)　for　exceeding　the　cycle　time　is　minimised　in　the　second　stage.　The　objective　is　to　minimise　the　expected　system　cost.　For　the　problem,　a　two-stage　distributionally　robust　formulation　is　devised,　to　minimise　the　worst-case　expected　system　cost　out　of　all　possible　probability　distributions.　Different　from　literature　that　focuses　on　approximation　methods　to　tackle　the　limited　distributional　information,　an　exact　method,　i.e.　the　cutting-plane　algorithm,　is　developed.　Numerical　results　show　that　compared　with　the　state-of-the-art　solution　methods,　our　developed　cutting-plane　algorithm　can　provide　more　reliable　solutions　in　term　of　the　robustness,　especially　in　some　extreme　cases.