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Zhang, X, Li, J, Yang, Z and Wang, X (2021) Collaborative optimization for loading operation planning and vessel traffic scheduling in dry bulk ports. Advanced Engineering Informatics, 51. ISSN 1474-0346
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Collaborative optimization for loading operation planning and vessel traffic scheduling in dry bulk ports .pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
While loading operation planning and vessel traffic scheduling are still deemed as two independent operations in practice, it has been realised that their collaborative optimization and coordination can improve port operation efficiency. It is because that two separate operations often result in vessels spending more waiting time when passing through channels and/or longer loading time at berth, and hence seriously affect the productivity and efficiency of ports. It is even worse in the case where multi-harbor basins share a restricted channel. Therefore, this paper aims to address the collaborative optimization of loading operation planning and vessel traffic scheduling (COLOPVTS) and to generate the optimal traffic scheduling scheme and loading operation plan for each vessel synchronously. Through analyzing the process of vessels entering and leaving dry bulk export ports, a multi-objective mathematical model of COLOPVTS is proposed. Due to the complexity of the model, a heuristic algorithm combining the Variable Neighborhood Search (VNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to solve the model. Finally, the computational results on the practical data of Phase I and Phase II terminals in Huanghua coal port are analysed to verify the rationality and effectiveness of the proposed model and algorithm.
| Item Type: | Article |
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| Uncontrolled Keywords: | 08 Information and Computing Sciences, 09 Engineering |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Elsevier |
| Date Deposited: | 13 Jan 2022 09:30 |
| Last Modified: | 18 Dec 2022 00:50 |
| DOI or ID number: | 10.1016/j.aei.2021.101489 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/16043 |
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