Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Heger, Jens; Voss, Thomas
• Is Part Of: International journal of production research. Volume 61:Issue 1(2023)
• Page Start: 147
• Page End: 161
• Publication Date: 2023-01-02
• Subjects: Sequencing rules -- dynamic adjustment -- simulation study -- reinforcement learning -- production planning and control; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2021.1943762
• Languages: English
• ISSNs: 0020-7543

Abstract

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%.