<p>Additive manufacturing delivers unique advantages over conventional methods, but efficient operation of large-scale 3D printing farms remains a challenge. This paper studies the integrated nesting and scheduling problem for non-identical parallel printers under calendar constraints that restrict batch start times to predefined operational shifts. We introduce a mixed-integer linear programming model capturing setup dependencies that require human intervention and continuous, unattended printing. The objective is to minimize the makespan by jointly optimizing part nesting and shift-aligned start times, thereby maximizing machine utilization around the clock. To address large instances, we propose a two-stage solution: a fast constructive heuristic generates an initial schedule, which is then refined by an adaptive large neighborhood search (ALNS). Computational results on problems solved optimally by the model show that the heuristic produces high-quality initial solutions in negligible time, while the ALNS achieves near-optimal schedules with an average deviation of 0.18% in a shorter time than the exact method. We further demonstrate the scalability of our approach on industrial-scale instances with up to 2000 parts and 200 printers. The findings confirm that incorporating calendar constraints into nesting and scheduling yields significant improvements in throughput and resource utilization in 3D printing farm operations.</p>