Stochastic　combinatorial　optimization　problems　are　usually　defined　as　planning　problems,　which　involve　purchasing　and　allocating　resources　in　order　to　meet　uncertain　needs.　For　example,　network　designers　need　to　make　their　best　guess　about　the　future　needs　of　the　network　and　purchase　capabilities　accordingly.　Facing　uncertain　in　the　future,　we　either　＂wait　and　see＂　changes,　or　postpone　decisions　about　resource　allocation　until　the　requirements　or　constraints　become　realized.　Specifically,　in　the　field　of　stochastic　combinatorial　optimization,　some　inputs　of　the　problems　are　uncertain,　but　follow　known　probability　distributions.　Our　goal　is　to　find　a　strategy　that　minimizes　the　expected　cost.　In　this　paper,　we　consider　the　two-stage　finite-scenario　stochastic　set　cover　problem　and　the　single　sink　rent-or-buy　problem　by　presenting　primal-dual　based　approximation　algorithms　for　these　two　problems　with　approximation　ratio　2　eta　and　4.39,　respectively,　where　eta　is　the　maximum　frequency　of　the　element　of　the　ground　set　in　the　set　cover　problem.