Adjusted Iterated Greedy for the optimization of additive manufacturing scheduling problems

• Extrusion-based production scheduling is investigated. Iterated Greedy extended for optimizing Additive Manufacturing Scheduling Problems, AMSP. The developed algorithm outperforms the state-of-the-art in AMSP literature. Directions future development of AMSPs are suggested. As a disruptive technology, additive manufacturing (AM) revolutionizing supply chains. AM consists producing 3-dimensional objects through layer-by-layer addition compound material based on digital models. operations differs from traditional (i.e., subtractive and injection molding) with single run involving several parts/geometries; this makes jobs heterogeneous. Limited studies have investigated Problems (AMSP). This study extends to solve considering single-machine setting. For purpose, computational mechanisms customized account AM-specific characteristics scheduling. Numerical analysis shows that vast majority best-found solutions yielded by Adjusted (AIG) both solution quality stability; outperformance becomes more significant an increase problem size. Statistical confirms AIG’s performance notably better than existing terms stability. concluded providing directions extend industrial reach 3D printing technology.