A hybrid approach to model additive manufacturing factory flow for the aerospace sector

Modular manufacturing is highly desirable due to its ability to quickly adapt to changing client demands and therefore its propensity to create high-margin products. However, this flexibility comes with the challenge of rapidly redesigning facility layouts when product demand or specifications change. Traditional Discrete Event Simulations (DES) are often time-consuming and computationally expensive, leading to delays in decision-making and potential impacts on revenue. To address this challenge, this paper proposes a hybrid approach that combines Linear Programming (LP) with DES for efficient and accurate facility design. A case study focusing on the adoption of Additive Manufacturing (AM) in the aerospace industry is presented to demonstrate the effectiveness of the proposed LP/DES approach. The specifications of the factory represent realistic requirements for a facility that's responsible for a sizeable market share of global aeroengine component production. The LP/DES methodology was employed to optimize factory flow using LP first, with two optimization techniques applied for comparison. The LP results were then fed directly into a DES framework to test the transient behaviour of the network when disruption was introduced. The results highlight the robustness and efficiency of the hybrid approach in optimizing factory operations and managing disruptions.