Fabric　dyeing　is　the　most　time-consuming　and　energy-intensive　process　in　textile　production　with　some　batch　processing　machines　(BPMs)　and　uncertainty.　In　this　study,　a　fuzzy　energy-efficient　parallel　BPMs　scheduling　problem　(FEPBSP)　with　machine　eligibility　and　sequence-dependent　setup　time　(SDST)　in　fabric　dyeing　process　is　investigated,　and　a　dynamical　teaching-learning-based　optimization　algorithm　(DTLBO)　is　proposed　to　simultaneously　optimize　the　total　agreement　index,　fuzzy　makespan,　and　total　fuzzy　energy　consumption.　In　DTLBO,　multiple　classes　are　constructed　by　non-dominated　sorting.　Dynamical　class　evolution　is　designed,　which　incorporates　diversified　search　among　students　and　adaptive　self-learning　of　teachers.　The　former　is　implemented　using　various　combinations　of　the　teacher　phase　and　the　learner　phase,　and　the　latter　is　achieved　through　teacher　quality　and　an　adaptive　threshold.　Additionally,　a　reinforcement　local　search　based　on　neighborhood　structure　dynamic　selection　is　also　applied.　Extensive　experiments　are　conducted,　and　the　computational　results　demonstrated　that　the　new　strategies　of　DTLBO　are　effective,　and　it　is　highly　competitive　in　solving　the　considered　problem.