2D Nesting and Scheduling in Metal Additive Manufacturing

Additive manufacturing (or 3D printing) is considered to be the future of manufacturing. Flexibility, accuracy, rapidness, cost efficiency and lightness are among the advantages those additive manufacturing provides. Astronauts can now produce their own tools and parts in the space with no waiting for another launch. Planning of additive manufacturing machines is a recent hot topic. Efficient use of such resources plays an important role to reduce the costs of additively manufactured parts as well as disseminate this technology making its advantages more apparent. This research addresses the metal additive manufacturing machine scheduling problem with multiple unidentical selective laser melting machines. Machines may have different specifications (dimension, speed, and cost parameters) and parts may have different characteristics (width, length, height, volume, release date and due date). The objective is to obtain a schedule such that total tardiness is minimized and parts are allocated on platforms (or building trays) with no overlap. A genetic algorithm approach is proposed to solve the problem within reasonable times. Results of the computational tests show the promising performance of the proposed method.