The　rapid　advancement　of　Industry　4.0　has　revolutionized　manufacturing,　shifting　production　from　centralized　control　to　decentralized,　intelligent　systems.　Smart　factories　are　now　expected　to　achieve　high　adaptability　and　resource　efficiency,　particularly　in　mass　customization　scenarios　where　production　schedules　must　accommodate　dynamic　and　personalized　demands.　To　address　the　challenges　of　dynamic　task　allocation,　uncertainty,　and　real-time　decision-making,　this　paper　proposes　Pathfinder,　a　deep　reinforcement　learning-based　scheduling　framework.　Pathfinder　models　scheduling　data　through　three　key　matrices:　execution　time　(the　time　required　for　a　job　to　complete),　completion　time　(the　actual　time　at　which　a　job　is　finished),　and　efficiency　(the　performance　of　executing　a　single　job).　By　leveraging　neural　networks,　Pathfinder　extracts　essential　features　from　these　matrices,　enabling　intelligent　decision-making　in　dynamic　production　environments.　Unlike　traditional　approaches　with　fixed　scheduling　rules,　Pathfinder　dynamically　selects　from　ten　diverse　scheduling　rules,　optimizing　decisions　based　on　real-time　environmental　conditions.　To　further　enhance　scheduling　efficiency,　a　specialized　reward　function　is　designed　to　support　dynamic　task　allocation　and　real-time　adjustments.　This　function　helps　Pathfinder　continuously　refine　its　scheduling　strategy,　improving　machine　utilization　and　minimizing　job　completion　times.　Through　reinforcement　learning,　Pathfinder　adapts　to　evolving　production　demands,　ensuring　robust　performance　in　real-world　applications.　Experimental　results　demonstrate　that　Pathfinder　outperforms　traditional　scheduling　approaches,　offering　improved　coordination　and　efficiency　in　smart　factories.　By　integrating　deep　reinforcement　learning,　adaptable　scheduling　strategies,　and　an　innovative　reward　function,　Pathfinder　provides　an　effective　solution　to　the　growing　challenges　of　multi-robot　job　scheduling　in　mass　customization　environments.　Copyright　©　2025　The　Authors.