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Uncertainty management in optimal disassembly planning through learning-based strategies

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Currently there is increasing consensus that one of the main issues differentiating remanufacturing from more traditional manufacturing processes is the need to effectively model and manage the high levels of uncertainty inherent in these new processes. Hence, the work presented in this paper concerns the issue of uncertainty modeling and management as it arises in the context of the optimal disassembly planning problem, one of the key problems to be addressed by remanufacturing processes. More specifically, the presented results formally establish that the theory of reinforcement learning, currently one of the most actively researched paradigms in the area of machine learning, constitutes a rigorous, efficient, and effectively implementable modeling framework for providing (near-)optimal solutions to the optimal disassembly problem, in the face of the aforementioned uncertainties. In addition, the proposed approach is exemplified and elucidated by application on a case study borrowed from the relevant literature.
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Keywords: (neuro-)dynamic programming; Disassembly planning; product recovery; reinforcement learning; uncertainty management

Document Type: Research Article

Affiliations: School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Publication date: 01 June 2007

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