Why triangular membership functions?
Fuzzy Sets and Systems
Learning in the presence of concept drift and hidden contexts
Machine Learning
Modeling and planning of disassembly processes
PROLAMAT '95 Proceedings of the IFIP WG5.3 international conference on Life-cycle modelling for innovative products and processes
Determining optimum disassembly sequences in electronic equipment
Computers and Industrial Engineering
VTS '02 Proceedings of the 20th IEEE VLSI Test Symposium
Analysis of an adaptive fuzzy system for disassembly process planning
ISEE '05 Proceedings of the International Symposium on Electronics and the Environment
Intelligent decision making in disassembly process based on fuzzy reasoning Petri nets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multilevel weighted fuzzy reasoning algorithm for expert systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy-Petri-net-based disassembly planning considering human factors
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Knowledge Representation Using High-Level Fuzzy Petri Nets
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A reasoning algorithm for high-level fuzzy Petri nets
IEEE Transactions on Fuzzy Systems
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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, this process is fraught with a high level of uncertainty (e.g., variations in product structure and condition, and human factors). The development of an effective modeling and management tool for such involved factors is critical in moving disassembly toward a more efficient and automated regime. This paper builds upon our previous work to undertake this problem. More specifically, a fuzzy Petri net model is introduced to explicitly represent the dynamics inherent in disassembly. Instead of presuming the pertinent data in the model is already known, a self-adaptive disassembly process planner and associated computationally effective algorithms are designed in a way to: 1) accumulate the past experience of predicting such data and, at the same time, 2) exploit the "knowledge" captured in the data to choose the best disassembly plan and improve the overall disassembly performance. To ensure the robustness of the learning procedure, variable memory length is further introduced. The proposed methodology and algorithms are illustrated through the disassembly of a batch of personal computers in a prototypical disassembly system.