This　paper　investigates　the　Dynamic　Flexible　Job　Shop　Scheduling　Problem　(DFJSP),　which　is　based　on　new　job　insertion,　machine　breakdowns,　changes　in　processing　time,　and　considering　the　state　of　Automated　Guided　Vehicles　(AGVs).　The　objective　is　to　minimize　the　maximum　completion　time　and　improve　on-time　completion　rates.　To　address　the　continuous　production　status　and　learn　the　most　suitable　actions　(scheduling　rules)　at　each　rescheduling　point,　a　Dueling　Double　Deep　Q　Network　(D3QN)　is　developed　to　solve　this　problem.　To　improve　the　quality　of　the　model　solutions,　a　MachineRank　algorithm　(MR)　is　proposed,　and　based　on　the　MR　algorithm,　seven　composite　scheduling　rules　are　introduced.　These　rules　aim　to　select　and　execute　the　optimal　operation　each　time　an　operation　is　completed　or　a　new　disturbance　occurs.　Additionally,　eight　general　state　features　are　proposed　to　represent　the　scheduling　status　at　the　rescheduling　point.　By　using　continuous　state　features　as　the　input　to　the　D3QN,　state-action　values　(Q-values)　for　each　scheduling　rule　can　be　obtained.　Numerical　experiments　were　conducted　on　a　large　number　of　instances　with　different　production　configurations,　and　the　results　demonstrated　the　superiority　and　generality　of　the　D3QN　compared　to　various　composite　rules,　other　advanced　scheduling　rules,　and　standard　Q-learning　agents.　The　effectiveness　and　rationality　of　the　dynamic　scheduling　trigger　rules　were　also　validated.　©　The　Author(s)　2024.