A Memory Fuzzy Learning for Uncertainty Management in Disassembly

Disassembly is of increasing importance in material and product recovery. However, this process is fraught with many uncertainties (e.g., variations in product structure and condition). In our previous work, such dynamics in disassembly process planning were addressed through an adaptive fuzzy system and associated algorithms. Building upon the work, this paper presents an enhanced fuzzy learning algorithm with variable memory length to ensure the robustness of the adaptation procedure. The proposed methodology and algorithm are illustrated via the disassembly of a batch of flashlights in a prototypical disassembly system