Abstract
In the highly competitive maritime liner business, liner companies face the ongoing risk of mismatches between supply and demand and intense price-cutting by their rivals. Most of them continuously work to improve the use of mega-vessels and form alliances to lower their operation costs and enhance their service network. International liners run long-haul services and fill their vessel slots with shipments from multiple trade lanes, chosen on the basis of shipment yields and empty repositioning from the perspective of local, regional and global slot planning operations. Here, a novel model for multi-echelon slot allocation was developed that accounts for the dynamics among local, regional-hub and global scales in terms of container loading and discharge at various vessels in multiple ports. A two-stage optimisation was proposed to improve usage and yield via slot exchange amongst liner companies in an alliance and cargo shifting amongst multiple trade lanes and service loops. Four optimisation methods for the three-echelon slot allocation were developed based on branch-and-bound search, genetic algorithm and deep neural network theories. The simulation results and model sensitivity of the developed algorithms were evaluated. Single-, two- and multiple-service routes with cargo shifting cases were simulated and analysed. The developed slot allocation model will assist trade and traffic planners in various echelons to coordinate and maximise slot usage and yield and ensure that cargo dimensions and weight fall within the cargo payload capacity and verified gross mass requirements, which further prevent vessel damage, excessive fuel usage and the unnecessary emission of greenhouse gases.
| Original language | English |
|---|---|
| Article number | 105983 |
| Journal | Computers and Operations Research |
| Volume | 148 |
| Online published | 28 Jul 2022 |
| DOIs | |
| Publication status | Published - Dec 2022 |
Funding
This work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference No.: UGC/FDS14/E07/18) and a grant from the Research Matching Grant Scheme (RMGS) in University Grant Committee of the Hong Kong Special Administrative Region, China.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 13 Climate Action
Research Keywords
- Deep neural network
- Genetic algorithm
- Maritime container transport
- Multi-echelon
- Optimisation
- Slot allocation
Publisher's Copyright Statement
- This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/
RGC Funding Information
- RGC-funded
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