A, Sivakumar ;N, Bagath Singh ;P, Sathiamurthi ;K S, Karthi Vinith . Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 67, n.12, p. 682-691, december 2021. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/extremal-micro-genetic-algorithm-model-for-time-cost-optimization-with-optimal-labour-productivity/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2021.7406.
A, S., N, B., P, S., & K S, K. (2021). Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity. Strojniški vestnik - Journal of Mechanical Engineering, 67(12), 682-691. doi:http://dx.doi.org/10.5545/sv-jme.2021.7406
@article{sv-jmesv-jme.2021.7406,
author = {Sivakumar A and Bagath Singh N and Sathiamurthi P and Karthi Vinith K S},
title = {Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {12},
year = {2021},
keywords = {labour productivity, equipment effectiveness, time-cost optimization, extremal optimization, micro genetic algorithm},
abstract = {In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively.},
issn = {0039-2480}, pages = {682-691}, doi = {10.5545/sv-jme.2021.7406},
url = {https://www.sv-jme.eu/article/extremal-micro-genetic-algorithm-model-for-time-cost-optimization-with-optimal-labour-productivity/}
}
A, S.,N, B.,P, S.,K S, K. 2021 December 67. Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 67:12
%A A, Sivakumar %A N, Bagath Singh %A P, Sathiamurthi %A K S, Karthi Vinith %D 2021 %T Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity %B 2021 %9 labour productivity, equipment effectiveness, time-cost optimization, extremal optimization, micro genetic algorithm %! Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity %K labour productivity, equipment effectiveness, time-cost optimization, extremal optimization, micro genetic algorithm %X In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively. %U https://www.sv-jme.eu/article/extremal-micro-genetic-algorithm-model-for-time-cost-optimization-with-optimal-labour-productivity/ %0 Journal Article %R 10.5545/sv-jme.2021.7406 %& 682 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 67 %N 12 %@ 0039-2480 %8 2021-12-21 %7 2021-12-21
A, Sivakumar, Bagath Singh N, Sathiamurthi P, & Karthi Vinith K S. "Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity." Strojniški vestnik - Journal of Mechanical Engineering [Online], 67.12 (2021): 682-691. Web. 19 Nov. 2024
TY - JOUR AU - A, Sivakumar AU - N, Bagath Singh AU - P, Sathiamurthi AU - K S, Karthi Vinith PY - 2021 TI - Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7406 KW - labour productivity, equipment effectiveness, time-cost optimization, extremal optimization, micro genetic algorithm N2 - In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively. UR - https://www.sv-jme.eu/article/extremal-micro-genetic-algorithm-model-for-time-cost-optimization-with-optimal-labour-productivity/
@article{{sv-jme}{sv-jme.2021.7406},
author = {A, S., N, B., P, S., K S, K.},
title = {Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {12},
year = {2021},
doi = {10.5545/sv-jme.2021.7406},
url = {https://www.sv-jme.eu/article/extremal-micro-genetic-algorithm-model-for-time-cost-optimization-with-optimal-labour-productivity/}
}
TY - JOUR AU - A, Sivakumar AU - N, Bagath Singh AU - P, Sathiamurthi AU - K S, Karthi Vinith PY - 2021/12/21 TI - Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 67, No 12 (2021): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7406 KW - labour productivity, equipment effectiveness, time-cost optimization, extremal optimization, micro genetic algorithm N2 - In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively. UR - https://www.sv-jme.eu/article/extremal-micro-genetic-algorithm-model-for-time-cost-optimization-with-optimal-labour-productivity/
A, Sivakumar, N, Bagath Singh, P, Sathiamurthi, AND K S, Karthi Vinith. "Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 67 Number 12 (21 December 2021)
Strojniški vestnik - Journal of Mechanical Engineering 67(2021)12, 682-691
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively.