With　increasing　market　competition,　integration　between　production　and　transportation　in　supply　chain　has　been　valued　to　improve　the　operational　performance.　Meanwhile,　the　distributed　manufacturing　has　emerged　as　a　modern　paradigm,　fostering　flexible　and　intelligent　development.　In　this　paper,　we　address　an　integrated　distributed　production　and　transportation　scheduling　problem　(IDPTSP),　taking　into　account　the　production　plan　in　distributed　heterogeneous　hybrid　flow-shops,　as　well　as　the　transportation　decisions　made　by　third-party　logistics　provider.　First,　the　objective　calculation　is　presented　to　minimize　total　costs　and　energy　consumption　during　production　and　transportation　process,　and　problem　property　is　analyzed　to　obtain　an　optimal　pickup　time　strategy.　Second,　a　knowledge-driven　cooperative　coevolutionary　algorithm　(KCCA)　is　proposed,　incorporating　multiple　problem-specific　heuristics　and　operators　based　on　the　characteristics　of　the　subproblems　in　IDPTSP.　Third,　Q　learning　assisted　cooperative　coevolutionary　search　is　proposed　via　analyzing　the　interconnections　among　different　subproblems　to　effectively　and　efficiently　explore　their　search　space.　Fourth,　local　intensification　search　with　multiple　operators　fusing　prior　knowledge　is　incorporated　for　low-density　regions　in　objective　space　to　enhance　exploitation　ability.　Extensive　experiments　are　carried　out　to　test　performances　of　the　KCCA　and　the　numerical　comparisons　demonstrate　effectiveness　of　the　specific　designs　and　superiority　of　the　KCCA　over　state-of-the-art　algorithms　in　solving　the　IDPTSP.　Note　to　Practitioners-Drawing　inspiration　from　a　real-life　case　in　electronics　supply,　the　focus　on　distributed　manufacturing　and　the　integration　of　manufacturing　and　logistics　has　garnered　significant　attention　from　managers　due　to　the　enterprises＇　need　to　reduce　intermediate　stocks　and　enhance　operational　performance.　The　primary　focus　of　enterprises　is　on　reducing　costs　during　production　and　transportation.　Additionally,　energy　consumption　has　become　a　crucial　consideration　due　to　rising　energy　costs　and　limited　energy　supplies.　However,　the　complexity　of　this　practical　scenario　has　significantly　increased　due　to　the　presence　of　multiple　subproblems　and　objectives.　Traditional　scheduling　methods　are　unable　to　effectively　and　efficiently　obtain　a　Pareto　front　with　convergence　and　diversity.　Based　on　the　characterises　of　the　scheduling　problem,　we　provided　a　knowledge-driven　cooperative　coevolutionary　algorithm　to　minimize　total　costs　and　energy　consumption　during　production　and　transportation　process.　At　production　stage,　factory　assignment　and　job　sequence　are　determined　in　distributed　heterogeneous　hybrid　flow-shops,　while　job　batching　and　vehicle　routing　are　determined　at　transportation　stage.　Through　the　analysis　of　problem　properties,　an　optimal　pickup　time　strategy　is　introduced　and　multiple　rules　and　search　operators　are　provided　to　enhance　optimization　efficiency.　Meanwhile,　the　framework　of　cooperative　coevolutionary　search　offers　an　effective　approach　to　addressing　such　complex　integrated　scheduling　problems.　From　experimental　comparisons,　the　effectiveness　of　the　proposed　algorithm　is　verified　surpassing　state-of-the-art　algorithms.　Therefore,　practitioners　can　benefit　from　non-dominated　schedules　characterized　by　reduced　costs　and　enhanced　energy　efficiency.