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Intelligent decision making in disassembly process based on fuzzy reasoning petri nets
Meimei Gao1, MengChu Zhou, Ying Tang
1Department of Mathematics and Computer Science, Seton Hall University, South Orange, NJ 07079, USA. gaomeime@shu.edu
Summary
This study introduces a fuzzy reasoning Petri net (FRPN) model for dynamic disassembly process planning. It enables adaptive decision-making for efficient material recycling, overcoming uncertainties in used products.
Area of Science:
- Engineering
- Computer Science
- Environmental Science
Background:
- Disassembly process planning is crucial for efficient material recycling and component reuse.
- Existing methods rely on predictive information, which is often unreliable due to product use-stage uncertainties.
- Dynamic adaptation to product status is necessary for realistic disassembly planning.
Purpose of the Study:
- To develop a model for dynamic and adaptive disassembly process planning.
- To address the challenge of uncertainty in used products for effective decision-making.
- To enable rapid and automated multicriterion decision-making in disassembly.
Main Methods:
- A fuzzy reasoning Petri net (FRPN) model is proposed to represent decision-making rules.
- A fuzzy reasoning algorithm based on the FRPN model is utilized.
- Decisions are made dynamically based on the real-time status of product components.
Main Results:
- The FRPN model allows for parallel consideration of multicriterion disassembly rules.
- The methodology facilitates automatic and quick decision-making.
- Intelligent decisions are made dynamically at each disassembly step, adapting to process changes.
Conclusions:
- The proposed FRPN model provides an adaptive approach to disassembly process planning under uncertainty.
- This method enhances the efficiency and practicality of material recycling and component reuse.
- The approach demonstrates effective dynamic decision-making for complex disassembly scenarios.
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