Current　Industrial　Internet　supports　the　sharing　of　information　on　heterogeneous　resources　and　elements　in　a　process　of　industrial　production.　It　enables　intelligent　production　processes　and　supports　cost-effective　scheduling.　However,　collaborative　manufacturing　and　scheduling　planning　for　enterprises　with　multiple　plants　cause　several　major　challenges　because　of　a　large　number　of　decision　variables　and　constraints　of　manufacturing　abilities　of　plants,　resources　of　production,　etc.　Existing　methods　cannot　comprehensively　optimize　the　cost　of　multiple　products　in　different　plants,　and　fail　to　consider　machine-level　optimization　of　tasks　of　manufacturing.　We　propose　a　comprehensive　machine-level　architecture　for　enterprises　with　multiple　plants.　Based　on　this　architecture,　we　formulate　a　limited　nonlinear　integer　optimization　problem　to　decrease　the　total　cost　of　transportation,　production,　and　sales.　In　it,　several　real-life　complicated　nonlinear　constraints　are　jointly　considered,　and　they　include　constraints　of　storage　space,　replacement　times,　pairing　production,　substitution,　and　order　fulfillment　rates.　To　solve　this　optimization　problem,　we　design　a　hybrid　meta-heuristic　optimization　algorithm　named　genetic　simulated　annealing-based　particle　swarm　optimizer　with　auto-encoders　(GSPAE).　Extensive　experiments　with　real-life　data　show　that　GSPAE　decreases　the　total　cost　by　25%　than　other　state-of-the-art　methods.