Yield and port performance shipping allocation model for revamp service deployments under a dynamic trading landscape

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number103279
Journal / PublicationTransportation Research Part C: Emerging Technologies
Volume130
Online published12 Jul 2021
Publication statusPublished - Sep 2021

Abstract

Uncertain trading landscape and subsequent dynamic changes in shipping patterns push ship liners to continuously revamp their service networks in a search for greater utilisation, yield, and profitability. Decision-making about revamping service deployments must become more rapid and accurate to achieve optimal, cost-efficient go-to-market networks for service routing with appropriate slot allocation. Traditional decision-making models focus on routing selection, fleet management, and container repositioning without considering changes in trading demand patterns, port performance, and trade lane yields. Here, a trade- and port-performance model for predicting shipping network yields was developed for revamp service deployment analyses, with optimised sets of allocated spaces on selected port calls at the service and shipment levels. Optimisation algorithms based on the branch-and-bound, genetic algorithm, and deep neural network techniques were developed for this model and applied to revamp services in Intra-Asia trade with consideration of the impact of the US–China trade war. The developed model assists trade pricing, network planning, yield management, and slot allocation in ship liner operations. Decision support models of shipping networks that enable ship liners to collaborate by forming alliances in service deployment can be explored as a further development.

Research Area(s)

  • Decision support model, Deep neural network, Genetic algorithms, Maritime transport, Optimisation, Ship liner service deployment, Vessel allocation, Yield

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