Abstract
A disassembly line balancing problem (DLBP) exists in the recycling process of end-of-life (EOL) products. It involves such factors as uncertainty of disassembly time and disassembly failure risk. Effective decisions can be made by taking them into full consideration. Under the constraints of disassembly precedence relationships and cycle time, this work establishes a stochastic multi-objective DLBP model subject to disassembly failure based on a disassembly AND/OR graph of EOL products. It considers disassembly failure risk and comprehensively evaluates the profit, energy consumption, average idle time of workstations, and hazard disassembly. Then, a new multi-objective discrete brainstorming optimizer that combines stochastic simulation is proposed for obtaining high- quality feasible solutions. Experimental results show the validity of the proposed algorithm. It outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimizer.
Original language | English (US) |
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Title of host publication | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1224-1229 |
Number of pages | 6 |
Volume | 2020-October |
ISBN (Electronic) | 9781728185262 |
DOIs | |
State | Published - Oct 11 2020 |
Event | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada Duration: Oct 11 2020 → Oct 14 2020 |
Conference
Conference | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
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Country/Territory | Canada |
City | Toronto |
Period | 10/11/20 → 10/14/20 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- Disassembly line balancing problem
- disassembly failure
- energy consumption
- genetic algorithm
- grey wolf optimizer
- multi-objective discrete brainstorming optimizer
- stochastic simulation