Sustainable design of on-demand supply chain network for additive manufacturing

This study proposes a novel optimization framework that simultaneously considers interdependence of flow networks, resource restrictions, and process-and-system level costs under a unified decision framework for the design and management of an integrated Additive Manufacturing (AM) supply chain network. A two-stage stochastic programming model is proposed that minimizes the facility location and capacity selection decisions at the first-stage prior to realizing any customer demand information. However, after the demand information is revealed, a number of second-stage decisions, such as optimal layer thickness for AM products, production, post-processing, procurement, storage, and transportation decisions, are made. Due to the need to solve our proposed optimization framework in a realistic-size network problem, a hybrid decomposition algorithm, combining the Sample Average Approximation algorithm with an Adaptive Large Neighborhood Search algorithm, is proposed. The performance of the proposed algorithm is validated by developing a case study using data from Alabama and Mississippi. Based on a set of numerical experiments, the effect of process-and-system level factors on the design and management of an AM supply chain network are analyzed. Numerous managerial insights, particularly on layer thickness, customer demand variability, mean demand variation, powder safety stock, and wastage rate on overall system performance, are gained which are crucial for the sustainment of this new manufacturing and supply chain paradigm.