Abstract
Given the fact that finding the optimal sequence in a flexible flow shop is usually an NP-hard problem, priority-based sequencing rules are applied in many real-world scenarios. In this contribution, an innovative reinforcement learning approach is used as a hyper-heuristic to dynamically adjust the k-values of the ATCS sequencing rule in a complex manufacturing scenario. For different product mixes as well as different utilisation levels, the reinforcement learning approach is trained and compared to the k-values found with an extensive simulation study. This contribution presents a human comprehensible hyper-heuristic, which is able to adjust the k-values to internal and external stimuli and can reduce the mean tardiness up to 5%.
| Original language | English |
|---|---|
| Journal | International Journal of Production Research |
| Volume | 61 |
| Issue number | 1 |
| Pages (from-to) | 147-161 |
| Number of pages | 15 |
| ISSN | 0020-7543 |
| DOIs | |
| Publication status | Published - 2023 |
Bibliographical note
Publisher Copyright:© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Titel der Ausgabe: Analytics and Machine Learning in Scheduling and Routing Optimization
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Research areas and keywords
- Engineering
- Sequencing rules
- dynamic adjustment
- simulation study
- reinforcement learning
- production planning and control
ASJC Scopus Subject Areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
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