Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis
AbstractIn ship construction, material costs constitute a substantial portion of the overall expenses. With the surging steel prices, the shipbuilding industry faces a pressing challenge. To counterbalance this issue, optimizing the structural components of ships has emerged as a viable solution. Ge...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2024.2324609 |
| _version_ | 1797266040911036416 |
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| author | Jos Istiyanto Gerry Liston Putra Muhammad Rifqi Ramadhan |
| author_facet | Jos Istiyanto Gerry Liston Putra Muhammad Rifqi Ramadhan |
| author_sort | Jos Istiyanto |
| collection | DOAJ |
| description | AbstractIn ship construction, material costs constitute a substantial portion of the overall expenses. With the surging steel prices, the shipbuilding industry faces a pressing challenge. To counterbalance this issue, optimizing the structural components of ships has emerged as a viable solution. Genetic Algorithm (GA) methods, known for their application in structural optimization, have demonstrated their potential. However, the protracted computational time associated with GA remains a limiting factor. This research introduces a novel approach by merging GA with Finite Element Method (FEM) for optimizing plate sizes, resulting in a hybrid GA system. Moreover, the study incorporates sensitivity analysis (SA) due to its proven efficacy in enhancing optimization processes involving multiple variables. The SA component investigates plate interrelationships and effectively clusters them. After this grouping, the hybrid GA executes parallel optimization of plates that influence each other under tension. By integrating SA, the optimization process becomes faster and more time-efficient, while preserving optimal manufacturing costs. Remarkably, this methodology culminates in a substantial reduction in computational time when contrasted with the hybrid GA approach devoid of SA, all the while maintaining a parallel manufacturing cost trajectory. In conclusion, this study presents an innovative hybrid GA approach, supplemented with SA, as an effective strategy for mitigating the escalating steel costs in shipbuilding. The amalgamation of GA, FEM and SA synergistically simplifies the optimization process, ensuring optimal results in a faster way. |
| first_indexed | 2024-04-25T00:54:22Z |
| format | Article |
| id | doaj.art-ee97693e1dcb4e3eae00c682ed9115c5 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-04-25T00:54:22Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-ee97693e1dcb4e3eae00c682ed9115c52024-03-11T12:21:45ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2024.2324609Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysisJos Istiyanto0Gerry Liston Putra1Muhammad Rifqi Ramadhan2Department of Mechanical Engineering, Universitas Indonesia, Depok, West Java, IndonesiaDepartment of Mechanical Engineering, Universitas Indonesia, Depok, West Java, IndonesiaDepartment of Mechanical Engineering, Universitas Indonesia, Depok, West Java, IndonesiaAbstractIn ship construction, material costs constitute a substantial portion of the overall expenses. With the surging steel prices, the shipbuilding industry faces a pressing challenge. To counterbalance this issue, optimizing the structural components of ships has emerged as a viable solution. Genetic Algorithm (GA) methods, known for their application in structural optimization, have demonstrated their potential. However, the protracted computational time associated with GA remains a limiting factor. This research introduces a novel approach by merging GA with Finite Element Method (FEM) for optimizing plate sizes, resulting in a hybrid GA system. Moreover, the study incorporates sensitivity analysis (SA) due to its proven efficacy in enhancing optimization processes involving multiple variables. The SA component investigates plate interrelationships and effectively clusters them. After this grouping, the hybrid GA executes parallel optimization of plates that influence each other under tension. By integrating SA, the optimization process becomes faster and more time-efficient, while preserving optimal manufacturing costs. Remarkably, this methodology culminates in a substantial reduction in computational time when contrasted with the hybrid GA approach devoid of SA, all the while maintaining a parallel manufacturing cost trajectory. In conclusion, this study presents an innovative hybrid GA approach, supplemented with SA, as an effective strategy for mitigating the escalating steel costs in shipbuilding. The amalgamation of GA, FEM and SA synergistically simplifies the optimization process, ensuring optimal results in a faster way.https://www.tandfonline.com/doi/10.1080/23311916.2024.2324609Material selectionsensitivity analysishybrid genetic algorithmcomputation timematerial costZhou Zude, Senior Editor, Wuhan University of Technology, China |
| spellingShingle | Jos Istiyanto Gerry Liston Putra Muhammad Rifqi Ramadhan Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis Cogent Engineering Material selection sensitivity analysis hybrid genetic algorithm computation time material cost Zhou Zude, Senior Editor, Wuhan University of Technology, China |
| title | Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis |
| title_full | Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis |
| title_fullStr | Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis |
| title_full_unstemmed | Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis |
| title_short | Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis |
| title_sort | enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis |
| topic | Material selection sensitivity analysis hybrid genetic algorithm computation time material cost Zhou Zude, Senior Editor, Wuhan University of Technology, China |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2024.2324609 |
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