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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.09088 |
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| _version_ | 1866914850768158720 |
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| author | Markhorst, Berend Berkhout, Joost Zocca, Alessandro Pruyn, Jeroen van der Mei, Rob |
| author_facet | Markhorst, Berend Berkhout, Joost Zocca, Alessandro Pruyn, Jeroen van der Mei, Rob |
| contents | The maritime industry must prepare for the energy transition from fossil fuels to sustainable alternatives. Making ships future-proof is necessary given their long lifetime, but it is also complex because the future fuel type is uncertain. Within this uncertainty, one typically overlooks pipe routing, although it is a crucial driver for design time and costs. Therefore, we propose a mathematical approach for modeling uncertainty in pipe routing with deterministic, stochastic, and robust optimization. All three models are based on state-of-the-art integer linear optimization models for the Stochastic Steiner Forest Problem and adjusted to the maritime domain using specific constraints for pipe routing. We compare the models using both artificial and realistic instances and show that considering uncertainty using stochastic optimization and robust optimization leads to cost reductions of up to 22% in our experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09088 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Future-proof ship pipe routing: navigating the energy transition Markhorst, Berend Berkhout, Joost Zocca, Alessandro Pruyn, Jeroen van der Mei, Rob Optimization and Control The maritime industry must prepare for the energy transition from fossil fuels to sustainable alternatives. Making ships future-proof is necessary given their long lifetime, but it is also complex because the future fuel type is uncertain. Within this uncertainty, one typically overlooks pipe routing, although it is a crucial driver for design time and costs. Therefore, we propose a mathematical approach for modeling uncertainty in pipe routing with deterministic, stochastic, and robust optimization. All three models are based on state-of-the-art integer linear optimization models for the Stochastic Steiner Forest Problem and adjusted to the maritime domain using specific constraints for pipe routing. We compare the models using both artificial and realistic instances and show that considering uncertainty using stochastic optimization and robust optimization leads to cost reductions of up to 22% in our experiments. |
| title | Future-proof ship pipe routing: navigating the energy transition |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2312.09088 |