Operations and Supply Chain Management | 2026
Authors: Kwakye J.; Sohn H.
DOI: 10.31387/oscm0640509
Journal: Operations and Supply Chain Management
Year: 2026
Publisher: Operations and Supply Chain Management Forum
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
Efficient scheduling and resource allocation in dental prosthesis supply chains are critical for improving patient outcomes and reducing turnaround times. Traditional workflows often suffer from delays due to fragmented routing, clinic bottlenecks, and limited visibility across production stages. To address these challenges, we develop a mixed-integer linear programming (MILP) model that jointly optimizes patient scheduling, routing decisions, and capacity constrained resource allocation in a digitally enabled, 3D printing-based prosthesis workflow. The model incorporates patient routing decisions (direct-to-lab vs. via-clinic), lab capacity constraints, material availability, and fixed delivery delays. The model minimizes lead time while enforcing fairness through worst-case constraints and deterministic delivery assumptions. Results from a baseline scenario involving 850 patients across a 45-day planning window show that the optimized system achieves balanced lab utilization, equitable lead time distribution, and full satisfaction of routing constraints. Comparative analysis highlights the significant advantage of direct-to-lab workflows over clinic-mediated routes. Scenario-based sensitivity analyses reveal the impact of routing strategies, inventory thresholds, and delivery penalties on system performance. The study demonstrates how simulation-informed optimization can guide strategic planning in healthcare logistics, offering a reproducible and adaptable framework for real-world deployment. © 2026
3D printing; Additive manufacturing; Dental supply chain; Digital dentistry; Lead time optimization; Mixed-integer linear programming; Patient scheduling; Scenario-based analysis