Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction
This paper proposes an integrated optimization model for vessel scheduling and routing. The objective is to maximize shipping company profits while considering profit volatility using the Conditional Value-at-Risk metric to master risks from demand fluctuations. Simultaneously, the model balances th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/2/207 |
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| _version_ | 1849719313608474624 |
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| author | Zhichao Cao Zhiwei Zhu Weike Lu Silin Zhang |
| author_facet | Zhichao Cao Zhiwei Zhu Weike Lu Silin Zhang |
| author_sort | Zhichao Cao |
| collection | DOAJ |
| description | This paper proposes an integrated optimization model for vessel scheduling and routing. The objective is to maximize shipping company profits while considering profit volatility using the Conditional Value-at-Risk metric to master risks from demand fluctuations. Simultaneously, the model balances the spot and contract container allocation by optimally adjusting shipping speeds so as to minimize carbon emissions. We account for vessel deployment, chartering costs, delay penalties, fuel expenses, and weather conditions to ensure the model’s compatibility with the practical transporting environment. In particular, a hybrid demand prediction model, combining long short-term memory and multi-scale network techniques, predicts spot and contract container volumes at ports, facilitating real-time allocation and more precise scheduling optimization. Two hybrid heuristics, one adaptive large-neighborhood search algorithm, and the Gurobi solver are devised and compared based on the efficiency and accuracy of solving the model. The results indicate that our optimization offers practical insights for shipping companies, enabling them to achieve a better trade-off between profits and risks, promoting a promising maritime transport career. |
| format | Article |
| id | doaj-art-13a2dfd366e14ed9a95fb121402870e5 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-13a2dfd366e14ed9a95fb121402870e52025-08-20T03:12:11ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113220710.3390/jmse13020207Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet PredictionZhichao Cao0Zhiwei Zhu1Weike Lu2Silin Zhang3School of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaIntelligent Urban Rail Engineering Research Center of Jiangsu Province, Suzhou 215137, ChinaSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaThis paper proposes an integrated optimization model for vessel scheduling and routing. The objective is to maximize shipping company profits while considering profit volatility using the Conditional Value-at-Risk metric to master risks from demand fluctuations. Simultaneously, the model balances the spot and contract container allocation by optimally adjusting shipping speeds so as to minimize carbon emissions. We account for vessel deployment, chartering costs, delay penalties, fuel expenses, and weather conditions to ensure the model’s compatibility with the practical transporting environment. In particular, a hybrid demand prediction model, combining long short-term memory and multi-scale network techniques, predicts spot and contract container volumes at ports, facilitating real-time allocation and more precise scheduling optimization. Two hybrid heuristics, one adaptive large-neighborhood search algorithm, and the Gurobi solver are devised and compared based on the efficiency and accuracy of solving the model. The results indicate that our optimization offers practical insights for shipping companies, enabling them to achieve a better trade-off between profits and risks, promoting a promising maritime transport career.https://www.mdpi.com/2077-1312/13/2/207risk-aware vessel schedulingrouting optimizationconditional value-at-risklong short-term memorymulti-scale network |
| spellingShingle | Zhichao Cao Zhiwei Zhu Weike Lu Silin Zhang Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction Journal of Marine Science and Engineering risk-aware vessel scheduling routing optimization conditional value-at-risk long short-term memory multi-scale network |
| title | Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction |
| title_full | Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction |
| title_fullStr | Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction |
| title_full_unstemmed | Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction |
| title_short | Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction |
| title_sort | risk aware vessel scheduling and routing optimization with cvar and lstm msnet prediction |
| topic | risk-aware vessel scheduling routing optimization conditional value-at-risk long short-term memory multi-scale network |
| url | https://www.mdpi.com/2077-1312/13/2/207 |
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