Additive manufacturing (AM), also known as 3D printing, enables the simultaneous production of different, complex, and customized parts directly from model data without prior tooling activities. These features allow manufacturers to respond quickly to varying customer demands and market trends. Furthermore, new opportunities arise in product design and innovation processes. In the early stages of AM, research primarily focused on developing and improving different AM technologies. However, research has also concentrated on production planning aspects emerging from these new technologies in recent years.

In this study, we examine the implications of considering AM machines in scheduling approaches and evaluate the relationship to well-known scheduling problems on batch processing machines. To this end, we first analyze various AM technologies and derive ways to model the processing time of production jobs. Moreover, we examine the impact of integrating AM-specific processing time models into scheduling problems. For this purpose, we present a mixed integer programming model aiming to minimize makespan on unrelated parallel AM machines. The model adapts principles from parallel machine scheduling as well as scheduling on batch processing machines. We compare different variants to calculate processing times for this model based on our findings and demonstrate the influences of AM on the scheduling tasks. To evaluate the performance of the model variants and their impacts on resulting schedules, we explore various instance settings. The results of our study highlight the benefits of explicitly incorporating AM features in problem formulations to substantially improve makespan in AM production facilities.