Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO
With the rapid development of global trade, the turnover of shipping containers has increased rapidly. How to use port resources reasonably and efficiently has become one of the main challenges that ports need to deal with when planning for the future. In order to develop scientific and efficient be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/12/1919 |
| _version_ | 1827638248148566016 |
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| author | Zhaohui Li Lin Wang Wenjia Piao Hao Jia Shan Dong Jiehan Zhang |
| author_facet | Zhaohui Li Lin Wang Wenjia Piao Hao Jia Shan Dong Jiehan Zhang |
| author_sort | Zhaohui Li |
| collection | DOAJ |
| description | With the rapid development of global trade, the turnover of shipping containers has increased rapidly. How to use port resources reasonably and efficiently has become one of the main challenges that ports need to deal with when planning for the future. In order to develop scientific and efficient berth plans to improve operational efficiency and service level, this paper proposes a hybrid prediction model based on Principal Component Analysis (PCA) and Extreme Learning Machine (ELM) optimized by Improved Particle Swarm Optimization (IPSO), namely, the PCA-IPSO-ELM model. After assessing the uncertain factors influencing the operation time of the container ship at berth, this work reduces the dimensionality of the investigational data by the PCA method. Aiming to solve easy premature convergence of the traditional particle swarm algorithm, this paper introduces an improved particle swarm optimization algorithm via dynamic adjustment of nonlinear parameters. This improved particle swarm algorithm is mainly used to optimize the weights and thresholds of the extreme learning machine. Thus, a PCA-IPSO-ELM model which aims to forecast the operation time of a container ship at berth, is constructed. Using the historical operation data of the Tianjin Port Container Shipping Company as the prediction sample, this PCA-IPSO-ELM model is compared and assessed with traditional models. The results show that compared with other models, the PCA-IPSO-ELM prediction model has the characteristics of high prediction accuracy, fast running rate and strong stability, and it has a higher coefficient of determination and a better fitting degree. |
| first_indexed | 2024-03-09T16:13:50Z |
| format | Article |
| id | doaj.art-5b0107a00c14442c9495161fbaadcb9d |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T16:13:50Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-5b0107a00c14442c9495161fbaadcb9d2023-11-24T15:56:35ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-12-011012191910.3390/jmse10121919Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSOZhaohui Li0Lin Wang1Wenjia Piao2Hao Jia3Shan Dong4Jiehan Zhang5School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSchool of Traffic and Electrical Engineering, Dalian University of Science and Technology, Dalian 116052, ChinaSchool of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, ChinaSouthampton Business School, University of Southampton, Southampton SO17 1BJ, UKWith the rapid development of global trade, the turnover of shipping containers has increased rapidly. How to use port resources reasonably and efficiently has become one of the main challenges that ports need to deal with when planning for the future. In order to develop scientific and efficient berth plans to improve operational efficiency and service level, this paper proposes a hybrid prediction model based on Principal Component Analysis (PCA) and Extreme Learning Machine (ELM) optimized by Improved Particle Swarm Optimization (IPSO), namely, the PCA-IPSO-ELM model. After assessing the uncertain factors influencing the operation time of the container ship at berth, this work reduces the dimensionality of the investigational data by the PCA method. Aiming to solve easy premature convergence of the traditional particle swarm algorithm, this paper introduces an improved particle swarm optimization algorithm via dynamic adjustment of nonlinear parameters. This improved particle swarm algorithm is mainly used to optimize the weights and thresholds of the extreme learning machine. Thus, a PCA-IPSO-ELM model which aims to forecast the operation time of a container ship at berth, is constructed. Using the historical operation data of the Tianjin Port Container Shipping Company as the prediction sample, this PCA-IPSO-ELM model is compared and assessed with traditional models. The results show that compared with other models, the PCA-IPSO-ELM prediction model has the characteristics of high prediction accuracy, fast running rate and strong stability, and it has a higher coefficient of determination and a better fitting degree.https://www.mdpi.com/2077-1312/10/12/1919prediction of operation time at berthprincipal component analysisextreme learning machineimproved particle swarm optimization |
| spellingShingle | Zhaohui Li Lin Wang Wenjia Piao Hao Jia Shan Dong Jiehan Zhang Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO Journal of Marine Science and Engineering prediction of operation time at berth principal component analysis extreme learning machine improved particle swarm optimization |
| title | Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO |
| title_full | Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO |
| title_fullStr | Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO |
| title_full_unstemmed | Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO |
| title_short | Prediction of Operation Time of Container Ship at Berth under Uncertain Factors Based on a Hybrid Model Combining PCA and ELM Optimized by IPSO |
| title_sort | prediction of operation time of container ship at berth under uncertain factors based on a hybrid model combining pca and elm optimized by ipso |
| topic | prediction of operation time at berth principal component analysis extreme learning machine improved particle swarm optimization |
| url | https://www.mdpi.com/2077-1312/10/12/1919 |
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