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RETRACTED ARTICLE: A model to predict bottlenecks over time in a remanufacturing system under uncertainty
Zheng Xue1, Tao Li2, Shitong Peng3
1School of Mechanical Engineering, Dalian University of Technology, Dalian, China.
Summary
This study introduces a novel method using a metamorphosis cellular neural network (CNT) and coupled map lattice (CML) algorithm to accurately predict bottleneck shifting in remanufacturing systems, improving throughput. The approach was validated using a connecting rod remanufacturing case study.
Area of Science:
- Manufacturing Engineering
- Systems Engineering
- Computational Science
Background:
- Bottleneck shifting prediction is crucial for optimizing remanufacturing system throughput.
- Dynamic bottleneck prediction is challenging due to processing variations and uncertain routes.
Purpose of the Study:
- To develop and validate a novel method for predicting bottleneck shifting in remanufacturing systems.
- To address the complexities of dynamic bottleneck identification in disturbed remanufacturing environments.
Main Methods:
- Utilized a metamorphosis cellular neural network (CNT) to model the remanufacturing process articulation.
- Employed a coupled map lattice (CML) algorithm to calculate dynamic bottleneck indicators.
- Validated the proposed method using a value-added connecting rod remanufacturing case study and Arena simulation.
Main Results:
- The integrated CNT and CML approach accurately predicts bottleneck shifting phenomena.
- The method demonstrates feasibility in complex, dynamic remanufacturing environments.
- Arena simulation confirmed the predictive accuracy of the proposed approach.
Conclusions:
- The developed metamorphosis CNT and CML algorithm provide a feasible and accurate solution for dynamic bottleneck prediction in remanufacturing.
- This predictive capability enhances throughput optimization and overall system performance in remanufacturing operations.
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