Analysis of an adaptive fuzzy system for disassembly process planning

As product lifecycles are getting shorter and shorter, manufacturers are facing a great deal of economic and political pressure to reclaim and recycle their obsolete products. Disassembly, as one of the natural solutions, is of increasing importance in material and product recovery. However, most of the existing works on disassembly process planning use a deterministic model to characterize the high levels of uncertainty (e.g., uncertainty in product structure and condition and human factors) inherent in the process. This paper builds upon our previous work (Tang, et al., 2004, Tang, et al. 2005) to explicitly address the dynamics in disassembly using fuzzy Petri nets. An adaptive fuzzy system with an enhanced learning method is proposed to predict the impact of human factors and product uncertainty on disassembly. Simulation software is also developed to validate the proposed method and the robustness of the adaptive fuzzy system.