With　increasing　energy　concerns　and　development　of　globalization,　energy-aware　scheduling　and　distributed　scheduling　have　become　significant　topics　in　modern　manufacturing.　However,　realistic　manufacturing　scenarios,　such　as　collaborative　scheduling　of　distributed　shops　and　limited　transportation　resources,　are　rarely　taken　into　account.　To　bridge　the　gap,　this　paper　addresses　the　energy-aware　distributed　flexible　job-shop　with　transportation　constraints　(EDFJSP-T)　and　proposes　a　feedback　learning-based　memetic　algorithm　(FLMA)　to　minimize　makespan　and　total　energy　consumption　simultaneously.　First,　a　mathematical　model　is　formulated　to　represent　the　relationship　between　different　sub-problems.　Additionally,　an　encoding　and　decoding　method　based　on　forward　insertion　is　designed　to　reduce　the　search　space　and　obtain　high-quality　schedules.　Second,　various　problem-specific　operators　are　designed　to　focus　on　different　sub-problems　and　objectives　to　enrich　search　patterns.　Third,　memetic　search　with　feedback　learning　is　proposed　via　introducing　observer　indexes　for　both　population　state　and　individual　state　to　adaptively　match　appropriate　operators　for　individuals.　Besides,　local　intensification　search　with　multiple　operators　is　incorporated　for　low-density　regions　to　further　improve　exploitation　ability.　The　parameter　setting　is　investigated　and　experimental　tests　are　carried　out　using　different　types　of　instances.　The　comparisons　demonstrate　the　effectiveness　of　the　feedback　learning　mechanism　and　the　superiority　of　the　FLMA　over　existing　algorithms　for　solving　the　EDFJSP-T.　IEEE