Since　the　outbreak　of　the　COVID-19　pandemic,　reshaping　global　supply　chains　for　bulk　mining　commodities,　such　as　coal,　copper,　and　iron　ore,　has　posed　significant　challenges.　The　complexity　and　multi-stakeholder　nature　of　mining　supply　chain　network　design　(MSCND)　require　innovative　optimization　approaches.　However,　traditional　literature　often　focuses　on　centralized　MSCND　strategies,　neglecting　the　competitive　dynamics　and　conflicts　of　interest　among　stakeholders.　To　address　this　gap,　this　study　introduces　a　bi-level　programming　(BLP)　model　for　decentralized　MSCND,　capturing　interactions　between　upper-level　ore　production　and　lower-level　ore　processing　enterprises.　To　overcome　the　computational　complexity　of　the　BLP　model,　we　develop　a　novel　hybrid　math-heuristic　algorithm　called　Sine　Cosine　and　Differential　Evolution　Algorithm　with　Constraint　Repair　Mechanism　(SCDEA-CRM).　The　proposed　SCDEA-CRM　integrates　the　search　mechanisms　of　sine　cosine　and　differential　evolution　algorithms,　along　with　a　novel　constraint　repair　mechanism　to　fix　infeasible　solutions　caused　by　chemical　composition　imbalances　between　raw　ores　and　products.　Numerical　experiments　demonstrate　the　SCDEA-CRM＇s　superior　performance　in　solving　the　BLP　model.　A　real-world　case　study　in　a　decentralized　iron　ore　supply　chain　validates　the　model＇s　practical　applicability　and　highlights　its　advantages　over　the　centralized　counterpart　model.　A　sensitivity　analysis　is　conducted　to　assess　the　impact　of　product　iron　content　variations　on　supply　chain　costs.　©　2024　Elsevier　Ltd