Production scheduling of additively manufactured metal parts

The production of metal products is one of the main areas where supply chains benefit from adopting additive manufacturing (AM). Optimizing the production process facilitates the widespread adoption of AM by improving know-how and reducing costs. This study offers a twofold contribution to facilitate the implementation of Additive Manufacturing Scheduling Problems (AMSPs) for producing metal parts. First, two mathematical formulations are proposed to enable the use of commercial solvers to optimize small- and medium-sized AMSPs. Second, a highly competitive solution algorithm called Tweaked Iterative Beam Search (TIBS) is developed to find (near-) optimal solutions to industry-scale problems. A total of 225 instances of various workloads are considered for numerical experiments, and the algorithm's performance is evaluated, comparing it with the baselines. In 165 small and medium-sized instances, TIBS yielded 71 optimal solutions and 106 best-found solutions. For large-scale cases, all of the best-found solutions were obtained by TIBS. The statistical results support the significance of the outcomes in the optimization performance.