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.　©　The　authors.　Published　by　EDP　Sciences,　ROADEF,　SMAI　2023.