Abstract
Today, it is difficult for companies to react to unforeseen events, e. g., global crises. Highly standardized manufacturing processes are particularly limited in their ability to react flexibly, creating a demand for more advanced workflow management techniques, e. g., extended by artificial intelligence methods. In this paper, we describe how Case-Based Reasoning (CBR) can be combined with automated planning to enhance flexibility in cyber-physical production workflows. We present a compositional adaptation method complemented with generative adaptation to resolve unexpected situations during workflow execution. This synergy is advantageous since CBR provides specific knowledge about already experienced situations, whereas planning assists with general knowledge about the domain. In an experimental evaluation, we show that CBR offers a good basis by reusing cases and by adapting them to better suit the current problem. The combination with automated planning further improves these results and, thus, contributes to enhance the flexibility of cyber-physical workflows.
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Notes
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More information about the smart factory model and a video can be found at https://iot.uni-trier.de.
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The PDDL 2.1 domain and all planning problems are available at https://gitlab.rlp.net/iot-lab-uni-trier/edoc-2022-idams-workshop.
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Malburg, L., Brand, F., Bergmann, R. (2023). Adaptive Management of Cyber-Physical Workflows by Means of Case-Based Reasoning and Automated Planning. In: Sales, T.P., Proper, H.A., Guizzardi, G., Montali, M., Maggi, F.M., Fonseca, C.M. (eds) Enterprise Design, Operations, and Computing. EDOC 2022 Workshops . EDOC 2022. Lecture Notes in Business Information Processing, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-031-26886-1_5
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