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Fostering the development of additive manufacturing (AM) in the context of mass production is a key factor to ensure its adoption in the industry. It should be remembered that this technology intrinsically makes it possible to produce parts with unexpected complexities in terms of shape and structure, but this comes at a price: time. To overcome this productivity barrier, AM technology providers are developing 3D printing machines with high-speed performance and mass reproduction means in a single run. Although such trends can be seen as a natural evolution of this technology with respect to current consumption patterns, it still remains a scientific issue on production planning to be tackled. The objective is to address the on-demand production planning of different AM parts in FabLabs composed of unrelated parallel 3D printers. A novel framework is introduced to consider part orientation, path planning, and part-to-printer assignment, with a specific focus on fused filament fabrication technique. By targeting a minimum production time, it exhibits reasoning algorithms implemented in a Python application. A case study with a batch of six non-identical parts and two fused filament fabrication 3D printers is introduced to illustrate the added value of the framework and its operational side.