Container pre-marshalling problem minimizing CV@R under uncertainty of ship arrival times
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916262734462976 |
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| author | Ikuma, Daiki Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi |
| author_facet | Ikuma, Daiki Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi |
| contents | This paper is concerned with the container pre-marshalling problem, which involves relocating containers in the storage area so that they can be efficiently loaded onto ships without reshuffles. In reality, however, ship arrival times are affected by various external factors, which can cause the order of container retrieval to be different from the initial plan. To represent such uncertainty, we generate multiple scenarios from a multivariate probability distribution of ship arrival times. We derive a mixed-integer linear optimization model to find an optimal container layout such that the conditional value-at-risk is minimized for the number of misplaced containers responsible for reshuffles. Moreover, we devise an exact algorithm based on the cutting-plane method to handle large-scale problems. Numerical experiments using synthetic datasets demonstrate that our method can produce high-quality container layouts compared with the conventional robust optimization model. Additionally, our algorithm can speed up the computation of solving large-scale problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17576 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Container pre-marshalling problem minimizing CV@R under uncertainty of ship arrival times Ikuma, Daiki Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi Optimization and Control Artificial Intelligence This paper is concerned with the container pre-marshalling problem, which involves relocating containers in the storage area so that they can be efficiently loaded onto ships without reshuffles. In reality, however, ship arrival times are affected by various external factors, which can cause the order of container retrieval to be different from the initial plan. To represent such uncertainty, we generate multiple scenarios from a multivariate probability distribution of ship arrival times. We derive a mixed-integer linear optimization model to find an optimal container layout such that the conditional value-at-risk is minimized for the number of misplaced containers responsible for reshuffles. Moreover, we devise an exact algorithm based on the cutting-plane method to handle large-scale problems. Numerical experiments using synthetic datasets demonstrate that our method can produce high-quality container layouts compared with the conventional robust optimization model. Additionally, our algorithm can speed up the computation of solving large-scale problems. |
| title | Container pre-marshalling problem minimizing CV@R under uncertainty of ship arrival times |
| topic | Optimization and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2405.17576 |