Scheduling-Location　(ScheLoc)　problem　considering　machine　location　and　job　scheduling　simultaneously　is　a　relatively　new　and　hot　topic.　The　existing　works　assume　that　only　one　machine　can　be　placed　at　a　location,　which　may　not　be　suitable　for　some　practical　applications.　Besides,　the　customer　credit　risk　which　largely　impacts　the　manufacturer＇s　profit　has　not　been　addressed　in　the　ScheLoc　problem.　Therefore,　in　this　work,　we　study　a　new　and　general　stochastic　parallel　machine　ScheLoc　problem　with　limited　location　capacity　and　customer　credit　risk.　The　problem　consists　of　determining　the　machine-to-location　assignment,　job　acceptance,　job-to-machine　assignment,　and　scheduling　of　accepted　jobs　on　each　machine.　The　objective　is　to　maximize　the　worst-case　probability　of　manufacturer＇s　profit　being　greater　than　or　equal　to　a　given　profit　(referred　to　as　the　profit　likelihood).　For　the　problem,　a　distributionally　robust　chance-constrained　(DRCC)　programming　model　is　proposed.　Then,　we　develop　two　model-based　approaches:　(1)　a　sample　average　approximation　(SAA)　method;　(2)　a　model-based　constructive　heuristic.　Numerical　results　of　300　instances　adapted　from　the　literature　show　the　average　profit　likelihood　proposed　by　the　constructive　heuristic　is　9.43%　higher　than　that　provided　by　the　SAA,　while　the　average　computation　time　of　the　constructive　heuristic　is　only　4.24%　of　that　needed　by　the　SAA.