Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity
Abstract
:1. Introduction
2. Method
2.1. Application Study
2.1.1. Model Definition
2.1.2. Well-Being and Productivity Evaluation
2.1.3. Mathematical Modeling of Optimization
2.1.4. Optimization Method
2.2. Knowledge Discovery
2.2.1. FPM
2.2.2. InfS-R
3. Results
3.1. Data Filtering
3.2. Data Clustering
3.3. Data Visualization
3.4. Knowledge Discovery
3.5. Knowledge Interpretation
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Manikin Number | Stature (mm) | Elbow Height (mm) | Sex |
---|---|---|---|
1 | 1629 | 984 | Female |
2 | 1755 | 1091 | Male |
3 | 1656 | 1020 | Female |
4 | 1780 | 1134 | Male |
5 | 1668 | 963 | Female |
6 | 1794 | 1068 | Male |
7 | 1800 | 1094 | Female |
8 | 1936 | 1221 | Male |
9 | 1602 | 949 | Female |
10 | 1731 | 1047 | Male |
11 | 1590 | 1006 | Female |
12 | 1717 | 1114 | Male |
13 | 1457 | 875 | Female |
14 | 1574 | 961 | Male |
Indices | Parameters | ||
---|---|---|---|
Welding spots | Welding time (s) | ||
Welding guns | Time to change welding gun (s) | ||
Welding sides | Time to change welding side (s) | ||
Manikins | Time to move to a far position (s) | ||
Welding sequence | Time to move to a near position (s) | ||
Variables | Previous gun: 1 if different, 0 if same | ||
Welding spot sequence | Previous side: 1 if different, 0 if same | ||
Welding gun used at each welding spot | 1 if previous spot is far, 0 if near | ||
Welding side at each welding spot | 1 if previous spot is near, 0 if far | ||
Objectives | RULA score for a manikin on a side with a welding gun at a welding spot | ||
Cycle time of welding process (s) | |||
Average RULA score per manikin in the welding process | |||
Average RULA score of all manikins in the welding process |
Optimization Algorithm | NSGA-II |
---|---|
Population size | 150 |
Child population size | 150 |
Tournament size | 2 |
Mutation operator | Polynomial |
Mutation probability | 0.2 |
Crossover probability | 0.9 |
Crossover operator | SBX |
Maximum iterations | 25,000 |
Result Selected | Sequence | ||
---|---|---|---|
47 | 3.09 | Spot sequence: 7-1-3-2-5-4-6 Gun sequence: 3-3-3-3-3-3-3 Side sequence: 1-1-1-1-2-2-2 | |
63 | 2.89 | Spot sequence: 4-5-6-7-1-2-3 Gun sequence: 3-3-3-3-2-2-2 Side sequence: 2-2-2-1-1-1-1 | |
85 | 2.86 | Spot sequence: 4-5-6-7-1-2-3 Gun sequence: 4-5-6-7-1-2-3 Side sequence: 2-2-2-1-1-1-1 |
X | X | |||||
X | X | |||||
X | X | |||||
X | X | X | X | |||
X | X | X | X | |||
X | X | X | X | |||
X | X | X |
FPM | ||||
---|---|---|---|---|
Case | Filtered Rules | Sig. (%) | Unsig. (%) | Ratio |
== 4 | 91.53 | 27.07 | 3.38 | |
> 2 | 100 | 40.85 | 2.45 | |
== 3 | 79.1 | 28.86 | 2.74 | |
== 3 | 75.14 | 7.58 | 9.91 | |
< 4 | 71.29 | 68.59 | 1.03 | |
> 2 | 64.36 | 45.51 | 1.41 | |
> 2 | 82.18 | 69.4 | 1.18 | |
> 2 | 52.48 | 17.38 | 3.02 | |
== 2 | 100 | 41.86 | 2.39 | |
== 2 | 100 | 31.93 | 3.13 | |
== 1 | 100 | 23.45 | 4.26 | |
== 1 | 100 | 0.73 | 136.99 | |
Worker diversity inclusion | == 2 | 93.84 | 38.17 | 2.46 |
== 2 | 93.84 | 26.94 | 3.48 | |
== 1 | 82.35 | 22.73 | 3.62 | |
== 1 | 75.95 | 0.32 | 237.34 |
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Iriondo Pascual, A.; Smedberg, H.; Högberg, D.; Syberfeldt, A.; Lämkull, D. Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity. Sustainability 2022, 14, 4894. https://doi.org/10.3390/su14094894
Iriondo Pascual A, Smedberg H, Högberg D, Syberfeldt A, Lämkull D. Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity. Sustainability. 2022; 14(9):4894. https://doi.org/10.3390/su14094894
Chicago/Turabian StyleIriondo Pascual, Aitor, Henrik Smedberg, Dan Högberg, Anna Syberfeldt, and Dan Lämkull. 2022. "Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity" Sustainability 14, no. 9: 4894. https://doi.org/10.3390/su14094894