Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing

Abstract

With the increasing prosperity of additive manufacturing, the 3D-printing shop scheduling problem has presented growing importance. The scheduling of such a shop is imperative for saving time and cost, but the problem is hard to solve, especially for simultaneous multi-part assignment and placement. This paper develops an improved evolutionary algorithm for application to additive manufacturing, by combining a genetic algorithm with a heuristic placement strategy to take into account the allocation and placement of parts integrally. The algorithm is designed also to enhance the optimisation efficiency by introducing an initialisation method based on the characteristics of the 3D printing process through the development of corresponding time calculation model. Experiments show that the developed algorithm can find better solutions compared with state-of-the-art algorithms such as simple genetic algorithm, particle swarm optimisation and heuristic algorithms.

Keywords:

• 3D printing
• scheduling
• processing planning
• genetic algorithm

Acknowledgments

The first author wishes to acknowledge the financial support of the China Scholarship Council (CSC).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under grant nos. 51675186 and 51175187, the Science & Technology Foundation of Guangdong Province under grant no. 2017A030223002, and the Fundamental Research Funds for the Central Universities under grant no. D2181830.