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Three-echelon slot allocation for yield and utilisation management in ship liner operations

  • Eugene Yin Cheung Wong*
  • , Kev Kwok Tung Ling
  • , Allen H. Tai
  • , Jasmine Siu Lee Lam
  • , X. Zhang
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

253 Downloads (CityUHK Scholars)

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 languageEnglish
Article number105983
JournalComputers and Operations Research
Volume148
Online published28 Jul 2022
DOIs
Publication statusPublished - 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)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  2. SDG 13 - Climate Action
    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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