A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization

Abstract

Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners' applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.