Multi-objective optimization analysis for part-to-Printer assignment in a network of 3D fused deposition modeling

Additive manufacturing (AM) has become popular for both industrial and personal use thanks to the freedom and benefits it provides in designing parts, reducing lead time, improving inventory, and part consolidation. However, few studies examine process planning issues in a network of AM systems that account for part-to-printer assignment involving multiple parts and printers with scheduling objectives. AM printers are characterized by different processes, heating technologies, and sizes of the build chamber, which impacts efficient and effective process planning and 3D printer scheduling. Given fixed, unrotated orientation of parts, we propose a decision aiding model based on a multi-objective optimization for a batch of parts and multiple printers. The proposed model considers operating cost, load balance among printers, total tardiness, and total number of unprinted parts as objectives in fused deposition modeling (FDM) process. A case study with automotive parts is used to verify and validate model functionalities. Then, the conflicting objectives of the model are analyzed using trade-off analysis to understand conflicting aspect among objectives. Next, a designed experiment with different number of parts and printers show the complexity and the generalization of the model.