A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals
Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container yard, in...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/1/45 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588208768024576 |
|---|---|
| author | Yifan Shen Beng Xuan Hongtao Hu Yansong Wu Ning Zhao Zhen Yang |
| author_facet | Yifan Shen Beng Xuan Hongtao Hu Yansong Wu Ning Zhao Zhen Yang |
| author_sort | Yifan Shen |
| collection | DOAJ |
| description | Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container yard, including wastage of yard space, excessive waiting time for external trucks, and conflicts with other production operations. To address these issues, a method based on a decomposed ensemble framework is proposed to predict short-term container quantities for gate-in operations at container terminal gates. The experiment compares the autoregressive integrated moving average (ARIMA) algorithm, the prophet algorithm, and the Long Short-Term Memory (LSTM) algorithm, with results indicating the clear advantage of Long Short-Term Memory in decomposed time series modeling. The introduction of this method is expected to enhance the accuracy and flexibility of terminal production planning, optimizing resource utilization. Contributions of this paper include the proposal of predictive models, a shipping route-based decomposed-ensemble framework, and confirmation of the superiority of Long Short-Term Memory in prediction through comparative analysis. These contributions are expected to improve terminal operational efficiency, reduce resource wastage, and better adapt to the highly stochastic gate-in operation environment. |
| format | Article |
| id | doaj-art-7516af5117504d219a03bb1f212bfeff |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-7516af5117504d219a03bb1f212bfeff2025-01-24T13:36:39ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011314510.3390/jmse13010045A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container TerminalsYifan Shen0Beng Xuan1Hongtao Hu2Yansong Wu3Ning Zhao4Zhen Yang5Ministry of Education Engineering Research Center for Container Supply Chain Technology, Shanghai Maritime University, Shanghai 201306, ChinaInstitute Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaXiamen Port Holding Group Co., Ltd., Xiamen 361013, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaInstitute Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaContainer terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container yard, including wastage of yard space, excessive waiting time for external trucks, and conflicts with other production operations. To address these issues, a method based on a decomposed ensemble framework is proposed to predict short-term container quantities for gate-in operations at container terminal gates. The experiment compares the autoregressive integrated moving average (ARIMA) algorithm, the prophet algorithm, and the Long Short-Term Memory (LSTM) algorithm, with results indicating the clear advantage of Long Short-Term Memory in decomposed time series modeling. The introduction of this method is expected to enhance the accuracy and flexibility of terminal production planning, optimizing resource utilization. Contributions of this paper include the proposal of predictive models, a shipping route-based decomposed-ensemble framework, and confirmation of the superiority of Long Short-Term Memory in prediction through comparative analysis. These contributions are expected to improve terminal operational efficiency, reduce resource wastage, and better adapt to the highly stochastic gate-in operation environment.https://www.mdpi.com/2077-1312/13/1/45container terminalgate-in operationsrandomnesstime series predictiondecomposed ensemble frameworkLSTM algorithm |
| spellingShingle | Yifan Shen Beng Xuan Hongtao Hu Yansong Wu Ning Zhao Zhen Yang A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals Journal of Marine Science and Engineering container terminal gate-in operations randomness time series prediction decomposed ensemble framework LSTM algorithm |
| title | A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals |
| title_full | A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals |
| title_fullStr | A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals |
| title_full_unstemmed | A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals |
| title_short | A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals |
| title_sort | decomposed ensemble prediction framework for gate in operations at container terminals |
| topic | container terminal gate-in operations randomness time series prediction decomposed ensemble framework LSTM algorithm |
| url | https://www.mdpi.com/2077-1312/13/1/45 |
| work_keys_str_mv | AT yifanshen adecomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT bengxuan adecomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT hongtaohu adecomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT yansongwu adecomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT ningzhao adecomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT zhenyang adecomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT yifanshen decomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT bengxuan decomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT hongtaohu decomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT yansongwu decomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT ningzhao decomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals AT zhenyang decomposedensemblepredictionframeworkforgateinoperationsatcontainerterminals |