Abstract
Modern day production sites for teeth manufacturing often utilize a high-level of automation and sophisticated machinery. Finding efficient machine schedules in such a production environment is a challenging task, as complex constraints need to be fulfilled and multiple cost objectives should be minimized.
This paper investigates a hyper-heuristic solution approach for the artificial teeth scheduling problem which originates from real-life production sites of the teeth manufacturing industry. We propose a collection of innovative low-level heuristic strategies which can be utilized by state-of-the-art selection-based hyper-heuristic strategies to efficiently solve practical problem instances. Furthermore, the paper introduces eight novel large-scale scheduling scenarios from the industry, which are included in the experimental evaluation of the proposed techniques.
An extensive set of experiments with well-known hyper-heuristics on benchmark instances shows that our methods improve state-of-the-art results for the large majority of the instances.
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Notes
- 1.
We make use of the Iverson bracket notation: \([P] = 1\), if \(P = true\) and \([P] = 0\) if \(P = false\).
- 2.
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The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged.
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Winter, F., Musliu, N. (2023). An Investigation of Hyper-Heuristic Approaches for Teeth Scheduling. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_20
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