Reconfigurable assembly systems (RAS) are designed to operate in highly volatile market environments, which establishes the need for constant redefinitions of the production planning activities. These planning activit...
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ISBN:
(纸本)9798350387032;9798350387025
Reconfigurable assembly systems (RAS) are designed to operate in highly volatile market environments, which establishes the need for constant redefinitions of the production planning activities. These planning activities are usually modeled as complex mathematical optimization problems, and as such, increased responsiveness of the system will be directly linked to the prompt and efficient solution of these problems. In this work, we investigate the use of per-instance algorithm configuration (PIAC) methods to select, at runtime, the best configuration of an algorithm designed to solve these optimization problems. We compare and evaluate the performance of Hydra, a state-of-the-art PIAC method designed for heterogeneous instance spaces, and a simpler Case Base Reasoning (CBR) approach to PIAC. Our experiments suggest that CBR methods for PIAC could be more suitable to highly homogeneous instance spaces, such as those observed in a RAS context, with reductions of up to 17% over the default algorithmconfiguration settings.
The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solvin...
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The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithmconfiguration in such homogeneous contexts traditionally adopts a "one-size-fits-all" approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra-a state-of-the-art PIAC method-and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method's performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithmconfiguration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra's methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra's inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR.
Automated algorithmconfiguration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However,...
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