Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing
Abstract
:1. Introduction
1.1. Problem Statement
1.2. Related Works
1.2.1. Characteristics of the Flexible Job-Shop Scheduling Problem (FJSP)
1.2.2. Computational Intelligence in FJSP
1.2.3. Optimizing GA for FJSP: A Comprehensive Review of Hyper-Parameter Adjustments
1.3. Our Contributions
2. Materials and Methods
2.1. The Construction of the Simulation System for Solid Wood Panel Processing
2.1.1. Analysis and Breakdown of the Solid Wood Panel Processing System Flow
2.1.2. Methodology for Constructing the Simulation System in Unity3D
2.2. Production Scheduling Framework for Solid Wood Panel Flexible Job Shop
- A solid wood panel can exclusively undergo processing on a specific equipment, following a specific procedure, at a particular moment.
- The equipment can process only one solid wood panel at a time.
- There exists a logical correlation and sequential limitations between the processing procedures of a solid wood panel.
- The processing of solid wood panels must be carried out continuously from the beginning to the end of the production process without any interruptions.
- The processing machinery remains operational without experiencing any malfunctions throughout the production process.
- The processing time utilized by the various processing machinery is identical for every process.
- Processes other than the five core processing steps need minimal time.
2.3. Adaptive Intelligent Optimization Genetic Algorithm (AIOGA)
2.3.1. Coding Strategy and Initialize the Population
2.3.2. Objective Function
2.3.3. Selection Operation
2.3.4. Crossover Operation
2.3.5. Mutation Operation
3. Results
3.1. Simulation System for Solid Wood Panel Processing
3.2. Application of AIOGA in Solving FJSP
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter Name | Light-Load Lifting AGV |
---|---|
Weight-carrying capacity | 2–10 m |
Positioning accuracy | 5–10 m |
Navigation accuracy | 6–15 m |
Driving method | Two-wheel differential drive |
Navigation mode | Magnetic navigation |
Parameter Name | M-20iA/35M | M-10iD/12 |
---|---|---|
Load capacity | 35.0 kg | 12.0 kg |
Number of control axes | 6-axis | 6-axis |
Reachable radius | 1813 mm | 1441 mm |
Transportable mass | 20 kg | 12 kg |
Repeat Positioning Accuracy | ±0.03 mm | ±0.02 mm |
Industrial Robot Quality | 250 kg | 145 kg |
Specification Parameter Name | Light-Load Lifting AGV |
---|---|
Lateral movement speed | 60 m/min |
Load capacity | 15 kg |
Positioning accuracy | ±5 mm |
Repeat positioning accuracy | ±0.1 mm |
Process | Line 1 | Line 2 | Line 3 |
---|---|---|---|
Outbound | 2 | 1 | 1 |
Machining | 5 | 3 | 1 |
Grinding | 4 | 2 | 1 |
Dedusting | 1 | 1 | 1 |
Packaging | 1 | 1 | 1 |
Algorithm Name | Best Case | Average Case | Worst Case | Workload Balance | Resource Utilization Rate | p |
---|---|---|---|---|---|---|
AIOGA | 90 | 90 | 92 | 0.73 | 88.69% | 0% |
GA | 149 | 151 | 155 | 0.68 | 82% | 39.60% |
ACO | 95 | 98 | 100 | 0.72 | 84% | 5.26% |
TS | 97 | 99 | 105 | 0.70 | 83% | 7.22% |
PSO | 92 | 95 | 96 | 0.69 | 85% | 2.17% |
SA | 109 | 111 | 120 | 0.74 | 87% | 17.43% |
EDA | 105 | 106 | 109 | 0.64 | 80% | 14.29% |
HSA | 100 | 103 | 105 | 0.67 | 81% | 10.00% |
CSA | 104 | 106 | 109 | 0.66 | 80% | 15.6% |
ANN | 86 | 87 | 89 | 0.75 | 88% | −4.65% |
DL | 84 | 85 | 87 | 0.76 | 90% | −7.14% |
APL | 230 | 231 | 240 | 0.31 | 63% | 60.87% |
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Yang, J.; Zheng, Y.; Wu, J. Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing. Sustainability 2024, 16, 3785. https://doi.org/10.3390/su16093785
Yang J, Zheng Y, Wu J. Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing. Sustainability. 2024; 16(9):3785. https://doi.org/10.3390/su16093785
Chicago/Turabian StyleYang, Jingzhe, Yili Zheng, and Jian Wu. 2024. "Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing" Sustainability 16, no. 9: 3785. https://doi.org/10.3390/su16093785