OPTIMIZATION PROPOSAL FOR TASK SEQUENCING IN THE PRINT QUEUE OF A 3D FARM WITH HETEROGENEOUS PARALLEL MACHINES

This paper proposes a conditional priority heuristic for order sequencing in a 3D Farm composed of FDM printers. The problem is modeled as a production scheduling problem on heterogeneous parallel machines, considering practical operational factors such as due dates, paid priority, printer efficiency, failure rate, production capacity, and setup times associated with filament changes, understood as the combination of material and color. The proposed heuristic starts from an initial FIFO queue and allows priority orders to overtake regular orders only when this movement does not violate the latest printing start time or the due date of the affected orders. After the queue is reorganized, orders are allocated to feasible printers by considering expected delay, setup requirements, and operational risk. The approach was evaluated through computational scenarios generated from real G-code files sliced in PrusaSlicer, including different order volumes and workload conditions. The proposed method was compared with FIFO and absolute-priority strategies. The results indicate that the conditional-priority heuristic preserves due dates while still benefiting priority orders, reduces setup times and filament changes, and avoids the delays imposed on regular orders by an unrestricted priority policy. These findings suggest that the proposed approach can provide a practical balance between commercial priority, delivery reliability, and operational efficiency in on-demand 3D printing farms.