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Titlebook: Adaptive Control of Bio-Inspired Manufacturing Systems; Dunbing Tang,Kun Zheng,Wenbin Gu Book 2020 Springer Nature Singapore Pte Ltd. 2020

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Bio-Inspired Manufacturing System Model,an music by Dr. JAAP KUNST, which until that moment had not come to my notice. A cursory glance was enough to convince me that the author was a very gifted man, who had made a sound and absolutely scientific study of the subject, and thereby made a valuable contribution, by means of careful observat
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2523-3386 cations.Offers several production optimization approaches baThis book introduces state-of-the-art models and methods based on the neuroendocrine-immune-inspired approaches in the field of manufacturing control systems. It develops various bio-inspired intelligent approaches for multiple applications
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Isabella Santi,Pablo Manfredi,Urs Jenaland applied to production scheduling problem in this Chapter. Dynamic scheduling problems are also analysed considering the random interference in shop floor scheduling, and the simulation results validate the effectiveness and feasibility.
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https://doi.org/10.1007/978-1-0716-1621-5ing functions for BIMC, disturbance detection and diagnosis mechanisms for BIMS, and strategies and concrete implementation steps. The results from comparative experiments verify that the disturbance handling approach improves the performance of the manufacturing system.
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Book 2020systems. It develops various bio-inspired intelligent approaches for multiple applications in order to efficiently generate production plans and control solutions and agilely deal with the frequent unexpected disturbances at the shop floor level. It also provides an introduction to bio-inspired manu
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Detecting Persister Awakening Determinantsased on hormonal regulation mechanism. This approach is tested on two scheduling problems. The results from the evaluation show that the proposed approach improves the scheduling quality compared with state-of-the-art on-line and off-line approaches.
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Emma Dawson,Emrah Şimşek,Minsu Kimroller is presented for reducing backlog variation and keeping a defined WIP level. The simulation results show that the presented control model is more responsive and robust against demand disturbances such as rush orders in manufacturing system.
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