With　the　rise　of　globalization　and　environmental　concerns,　distributed　scheduling　and　energy-efficient　scheduling　have　become　crucial　topics　in　the　informational　manufacturing　system.　Additionally,　the　growing　consideration　about　realistic　constraints,　such　as　transportation　time　and　finite　transportation　resources,　has　made　the　scheduling　problem　increasingly　complex.　Facing　these　challenges,　special　mechanisms　are　required　to　improve　the　efficiency　of　solving　algorithms.　In　this　paper,　a　bi-learning　evolutionary　algorithm　(BLEA)　is　proposed　to　solve　the　distributed　energy-efficient　flexible　job　shop　problem　with　transportation　constraints　(DEFJSP-T).　Firstly,　we　integrate　statistical　learning　(SL)　and　evolutionary　learning　(EL)　in　the　framework,　while　decomposition　and　Pareto　dominance　methods　are　employed　in　different　stages　to　handle　conflicting　objectives.　During　the　SL　stage,　probability　models　are　established　to　statistically　search　for　advantageous　substructures　on　each　weight　vector,　and　an　update　mechanism　is　devised　to　improve　the　exploration.　In　the　EL　stage,　the　genetic　operators　are　introduced　and　an　improved　local　search　that　takes　into　account　the　problem　properties　is　proposed　to　realize　sufficient　exploitation.　Finally,　according　to　the　performance　of　the　SL,　a　novel　switching　mechanism　between　SL　and　EL　is　designed　to　ensure　the　rational　allocation　of　computing　resources.　Extensive　experiments　are　conducted　to　test　the　performances　of　the　BLEA.　The　statistical　comparison　shows　that　the　BLEA　is　superior　in　solving　the　DEFJSP-T　in　terms　of　efficiency　and　effectiveness.　IEEE