A machine learning approach for optimal disassembly planning
International Journal of Computer Integrated Manufacturing - THE CHALLENGES OF MANUFACTURING IN THE GLOBALLY INTEGRATED ECONOMY. GUEST EDITOR: ROBIN G. QIU
Learning-based disassembly process planner for uncertainty management
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line
Robotics and Computer-Integrated Manufacturing
Simplified swarm optimization in disassembly sequencing problems with learning effects
Computers and Operations Research
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Disassembly, as the process of systematic removal of desirable constituent parts from an assembly, is of growing importance due to the increasing environmental and economic pressure. Although disassembly in practice is manual and labor intensive, little attention has been paid to the human intervention in the disassembly process. This paper addresses this deficiency by developing a fuzzy attributed Petri net (FAPN) model to mathematically represent uncertainty in disassembly due to a large amount of human intervention. An algorithm based upon this model is further proposed for optimal disassembly planning with a view to making the technique more applicable to real industry settings. The benefit of the proposed model and algorithm is illustrated through the disassembly of a personal computer (PC) in a prototypical disassembly system