Rigid　environmental　regulations,　sustainable　development　and　green　manufacturing　have　been　attracting　public　attention.　Thus　disassembly　line　balancing　problem　(DLBP),　which　plays　a　significant　role　in　end-of-life　(EOL)　product　recycling,　is　a　hot　research　topic.　Besides,　the　emergence　of　Industry　4.0　provides　new　paradigms　for　the　manufacturing　systems,　i.e.,　integrating　the　physical　and　decisional　aspects　to　support　their　in-time　communication　and　cooperation　in　manufacturing　systems.　Such　new　paradigms　bring　challenges　for　manufacturing　process,　including　the　DLBP.　Especially,　due　to　the　tight　links　between　human　and　manufacturing,　workload　balance　is　considered　as　one　objective　in　this　paper.　This　work　studies　an　entropy-based　bi-objective　DLBP,　to　minimize　(1)　the　total　system　cost　and　(2)　the　workload　balance　measured　by　a　nonlinear　entropy　function.　A　bi-objective　mixed　integer　nonlinear　programming　(MINLP)　formulation　is　first　proposed,　and　then　the　second-order　Taylor　series　expansion　approximation　is　applied　to　approximate　the　logarithmic　objective.　The　epsilon-constraint　method　is　further　applied　to　obtain　the　Pareto　front.　A　case　study　is　conducted　to　evaluate　the　application　of　the　proposed　method,　and　some　managerial　insights　are　drawn　as　well.