A Novel Simulated Annealing-Based Hyper-Heuristic Algorithm for Stochastic Parallel Disassembly Line Balancing in
Youxi Hu1, Chao Liu1, Ming Zhang1
1College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK.
Sensors (Basel, Switzerland)
|February 11, 2023
Summary
This study addresses the disassembly line balancing problem (DLBP) for stochastic parallel complete lines. A new hyper-heuristic algorithm optimizes disassembly lines for efficiency and profit, validated with gearbox disassembly.
Area of Science:
- Industrial Engineering
- Operations Research
- Sustainable Manufacturing
Background:
- Remanufacturing extends product lifecycles and residual value.
- Disassembly is crucial for component retrieval in remanufacturing.
- Disassembly line balancing problem (DLBP) is key to efficient remanufacturing operations.
Purpose of the Study:
- Extend the basic DLBP model to stochastic parallel complete disassembly lines (DLBP-SP).
- Develop a multi-objective optimization algorithm for DLBP-SP.
- Evaluate the proposed algorithm's performance and robustness.
Main Methods:
- Developed an extended mathematical model for DLBP-SP.
- Proposed a simulated annealing-based hyper-heuristic (HH) algorithm.
- Conducted computational experiments and a gearbox disassembly case study.
Main Results:
- The proposed HH algorithm effectively optimizes DLBP-SP considering workstations, workload, and profit.
- Validated the algorithm's feasibility, superiority, stability, and robustness.
- Demonstrated the application of DLBP to industrial equipment, specifically gearboxes.
Conclusions:
- The novel HH algorithm provides a robust solution for multi-objective DLBP-SP.
- The research expands DLBP applications to complex industrial equipment.
- Optimized disassembly lines enhance the cost-effectiveness and efficiency of remanufacturing.
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