In　the　stockyard　of　dry　bulk　materials　such　as　coal　and　iron　ore,　multiple　stacker-reclaimers　operate　collaboratively　to　enhance　stockyard　productivity.　However,　effectively　planning　the　routes　for　these　interconnected　machines　presents　a　significant　challenge.　We　propose　a　new　modeling　framework　for　this　conflict-free　routing　problem　in　which　the　reclaiming　process　is　modeled　in　a　so-called　time-space　network　(TSN)　framework.　The　formulated　optimization　problem　is　mixed　integer　programming　(MIP),　which　has　been　proven　NP-hard.　To　address　its　computational　efficiency,　we　develop　a　two-level　metaheuristic　algorithm　to　simplify　the　encoding　complexity　of　the　original　problem.　In　the　developed　two-level　algorithm,　a　task　priority　ordering　candidate　is　listed　at　the　top　level,　while　the　detailed　conflict-free　routes　are　constructed　at　the　bottom　level　using　an　insertion-based　conflict-free　reclaiming　and　customized　route　improvement.　The　developed　metaheuristic　is　tested　on　a　large　number　of　instances　in　comparison　with　the　Gurobi　solver　and　three　commonly　used　methods　for　such　a　routing　problem.　The　experimental　results　show　that　the　proposed　algorithm　outperforms　the　other　four　methods　regarding　the　solution　quality　and　the　computation　time.　Note　to　Practitioners-This　study　is　motivated　by　the　need　for　efficient　route　planning　of　interconnected　reclaimers　in　the　stockyard　of　dry　bulk　materials　such　as　coal　and　iron　ore.　To　address　this　problem,　we　present　a　novel　modeling　framework　for　addressing　the　conflict-free　routing　problem,　wherein　the　reclaiming　process　is　intricately　modeled　within　a　so-called　time-space　network　framework.　Solving　the　resulting　mixed-integer　programming　is　of　high　computational　complexity;　therefore,　we　have　engineered　a　two-level　metaheuristic　framework　to　reduce　its　complexity.　The　developed　methodology　has　been　extensively　tested　on　instances　featuring　real-world　stockyard　operations　and　contributes　to　terminal　operators　by　improving　productivity　and　resulting　in　more　economic　benefits.　Future　research　will　integrate　real-time　data　and　dynamic　optimization　techniques　to　facilitate　routing　decisions　that　can　adapt　to　evolving　operational　conditions　and　stockyard　configurations.