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Addressing over-correction in adaptive card-based pull control systems
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DOI：10.1080/0951192X.2018.1509132.png)
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Abstract
En 中文
Adaptive card control strategy-based heuristics are used to change the number of cards dynamically in response to stochastic and fluctuated customers' demands. However, when the decisions are taken in real-time and without the use of forecasts, existing heuristics may yield to change too often the number of cards. This over-correction, related to system nervousness, can induce undesirable effects for the workshop. Our literature analysis underlines that, despite nervousness has been discussed in other industrial areas, it seems not to have been studied in the context of adaptive pull control systems. We first introduce and discuss nervousness in the context of adaptive pull control systems. We identify the main factor that induces nervousness and its consequences. To reduce nervousness, we propose a new approach, which relies both on an adaptive freezing interval and a multi-objective simulation optimisation technique. We first show the relevance of the proposed approach through a comparison with an adaptive Kanban system taken from the literature. This comparison shows that our approach yields better results. In addition, the resulting Pareto front offers flexibility to the decision-maker. The suggested approach can be useful: for managers to better implement their adaptive pull systems, and for decision-makers to define operational procedures taking nervousness into account.
Keywords:
Adaptive control
dynamic production control
genetic algorithms
just in time
Kanban
simulation
adaptive card control
reactive Kanban
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