Adjusted Iterated Greedy for the optimization of additive manufacturing scheduling problems

Abstract

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