Time-of-use　(TOU)　electricity　pricing　policy　is　widely　encountered　in　the　world,　which　provides　new　opportunities　for　power-intensive　enterprises　to　save　their　energy　cost.　A　good　trade-off　between　the　total　electricity　cost　and　production　efficiency　is　desired　by　decision　makers.　Thiswork　addresses　an　energy-conscious　bi-objective　single-machine　batch　scheduling　problem　under　TOU　electricity　tariffs,　in　which　electricity　price　varies　with　time.　The　objective　of　the　problem　is　to　simultaneously　minimize　total　electricity　cost　and　makespan.　Due　to　its　strong　NP-hard　nature,　two　fast　new　epsilon-constraint-based　constructive　heuristic　algorithms　are　developed　to　solve　it.　The　core　idea　is　to　transform　the　bi-objective　problem　into　a　series　of　single-objective　problems　that　are　fast　and　heuristically　solved　to　obtain　an　approximate　Pareto　front.　Especially,　for　each　transformed　single-objective　problem,　two　novel　constructive　heuristic　algorithms　are　proposed　by　solving　a　series　of　multiple　knapsack　problems　and　0-1　knapsack　problems,　respectively.　Computational　results　on　145　benchmark　and　80　newly　generated　larger-scale　instances　show　that　the　proposed　algorithms　are　quite　efficient　and　are　able　to　find　high-quality　Pareto　solutions　for　large-scale　problems　with　up　to　200　batches.