Modelling and controlling uncertainty in optimal disassembly planning through reinforcement learning

Currently there is increasing consensus that one of the main issues differentiating the remanufacturing from the more traditional manufacturing processes is the need to effectively model and manage the high levels of uncertainty inherent in these new processes. The work presented in this paper formally establishes that the theory of reinforcement learning, one of the most actively researched areas in computational learning theory, constitutes a rigorous, effectively implementable modelling framework for providing (near) optimal solutions to the optimal disassembly planning (ODP) problem, one of the key problems to be addressed by remanufacturing processes, in the face of the aforementioned uncertainties. The developed results are exemplified and validated by application on a case study borrowed from the relevant literature.