This　study　investigates　a　biobjective　integrated　parallel　machine　scheduling　and　location　problem.　It　aims　to　place　machines　on　a　set　of　candidate　locations,　assign　jobs　dispersed　in　different　locations　to　the　placed　machines,　and　sequence　them　while　minimizing　the　maximum　completion　time,　i.e.,　makespan,　and　the　location　cost.　For　the　challenging　NP-hard　problem,　we　first　develop　an　improved　mixed-integer　linear　program.　Then,　several　inequalities　are　proposed　to　further　strengthen　it.　To　more　effectively　and　efficiently　solve　practical-size　instances,　a　new　iterative　two-stage　heuristic　algorithm　based　on　　$\varepsilon$　-constraint　is　proposed.　Extensive　experimental　results　demonstrate　that　1)　the　improved　model　with　valid　inequalities　can　solve　　$78.4\%$　　of　　$500$　　benchmark　instances,　more　than　　$29.8\%$　　for　the　state-of-the-art　one　and　the　Pareto　solutions　obtained　by　the　former　are　much　superior　to　that　of　the　latter　and　2)　the　proposed　iterative　two-stage　heuristic　algorithm　can　solve　all　benchmark　instances　and　its　performance　is　significantly　superior　to　the　widely　adapted　nondominated　sorting　genetic　algorithm　2　in　obtaining　high-quality　Pareto　solutions.　IEEE