Demand　fluctuates　in　actual　production.　When　manufacturers　face　demand　under　their　maximum　capacity,　suspension　shifts　are　crucial　for　cost　reduction　and　on-time　delivery.　In　this　case,　suspension　shifts　are　needed　to　minimize　idle　time　and　prevent　inventory　buildup.　Thus,　it　is　essential　to　integrate　suspension　shifts　with　scheduling　under　an　uncertain　production　environment.　This　paper　addresses　the　two-stage　hybrid　flow　shop　scheduling　problem　(THFSP)　with　suspension　shifts　under　uncertain　processing　times,　aiming　to　minimize　the　weighted　sum　of　earliness　and　tardiness.　We　develop　a　stochastic　integer　programming　model　and　validate　it　using　the　Gurobi　solver.　Additionally,　we　propose　a　dual-space　co-evolutionary　biased　random　key　genetic　algorithm　(DCE-BRKGA)　with　parallel　evolution　of　solutions　and　scenarios.　Considering　decision-makers＇　risk　preferences,　we　use　both　average　and　pessimistic　criteria　for　fitness　evaluation,　generating　two　types　of　solutions　and　scenario　populations.　Testing　with　28　datasets,　we　use　the　value　of　the　stochastic　solution　(VSS)　and　the　expected　value　of　perfect　information　(EVPI)　to　quantify　benefits.　Compared　to　the　average　scenario,　the　VSS　shows　that　the　proposed　algorithm　achieves　additional　value　gains　of　0.9%　to　69.9%.　Furthermore,　the　EVPI　indicates　that　after　eliminating　uncertainty,　the　algorithm　yields　potential　improvements　of　2.4%　to　20.3%.　These　findings　indicate　that　DCE-BRKGA　effectively　supports　varying　decision-making　risk　preferences,　providing　robust　solutions　even　without　known　processing　time　distributions.