This　work　addresses　a　new　bi-objective　parallel　machine　selection　and　job　scheduling　problem　with　release　dates　and　resource　consumption.　It　consists　in　optimally　selecting　subcontractors　(machines)　from　a　set　of　geographically　dispersed　locations　and　scheduling　the　orders　(jobs)　to　the　selected　subcontractors　for　processing　while　meeting　the　order　release　dates　and　resource　consumption　restrictions,　so　as　to　simultaneously　minimize　the　maximum　completion　time,　i.e.,　the　makespan,　and　the　total　cost　including　machine　usage　cost　and　resource　consumption　cost.　The　problem　is　first　formulated　into　a　bi-objective　mixed　-integer　linear　program　based　on　linear　ordering　(LO-MILP),　and　then　valid　inequalities　are　explored　based　on　property　analysis.　To　solve　it,　an　e　-constraint　method　based　on　LO-MILP　(e-LO-MILP)　is　first　proposed.　To　more　efficiently　solve　it,　we　also　develop　a　tailored　logic　-based　Benders　decomposition　combined　with　e　-constraint　method　(e-LBBD)　where　a　novel　method　to　obtain　a　tight　lower　bound　of　the　identical　parallel　machine　with　machine　-dependent　release　dates　and　some　problem　-specific　cuts　are　proposed.　Numerical　experiments　on　an　illustrative　example　are　conducted　to　show　the　applicability　of　the　model　and　algorithm　and　intuitively　reveal　the　trade-off　between　production　efficiency　and　cost.　Experimental　results　on　200　instances　with　up　to　100　orders　demonstrate　that　e-LBBD　can　reduce　the　computation　time　by　about　28.92%　compared　to　e-LO-MILP　and　yields　better　Pareto　solutions　than　e-LO-MILP　and　the　well-known　non　-dominated　sorted　genetic　algorithm　II　do.