Automated Production Scheduling for Artificial Teeth Manufacturing

Authors

  • Felix Winter TU Wien
  • Christoph Mrkvicka MCP GmbH
  • Nysret Musliu TU Wien
  • Jakob Preininger TU Wien

Keywords:

Description And Modeling Of Novel Application Domains, Industry / Application Challenge Problems, Evaluation, Testing, And Validation Of Planning And Scheduling Applications

Abstract

In industrial artificial teeth manufacturing, nowadays a high level of automation is utilized to produce a large quantity of teeth in short production cycles. As a large variety of different product shapes and colors have to be processed on a single machine, the creation of efficient production schedules becomes a very challenging task. Due to the complexity of the problem and several cost minimization objectives that need to be considered, there usually is a large potential to improve the currently manually created schedules with automated solution methods. In this paper, we formally specify and solve a novel challenging real-life machine batch scheduling problem from the area of artificial teeth manufacturing. Additionally, we provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem. To efficiently solve the problem, we propose an innovative construction heuristic and metaheuristic approach as well as an exact method using constraint programming. An extensive experimental evaluation shows that exact techniques can efficiently solve small scheduling scenarios and can provide optimal solutions for four instances. Furthermore, we show that the proposed metaheuristic approach is able to reach optimal results for small instances and can find high quality solutions also for large real-life benchmark instances.

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Published

2021-05-17

How to Cite

Winter, F., Mrkvicka, C., Musliu, N., & Preininger, J. (2021). Automated Production Scheduling for Artificial Teeth Manufacturing. Proceedings of the International Conference on Automated Planning and Scheduling, 31(1), 500-508. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/15997