Optimisation of containerised air cargo forwarding plans considering a hub consolidation process with cargo loading

Abstract

Air cargo plays an important role in supporting global supply chains; this becomes more vital when facing uncertainties in a crisis such as the COVID-19 pandemic. This motivates our study on air cargo forwarding plans, considering demand uncertainties and economic conditions. Cargos are placed into air containers based on weights and volumes, and then flown from regional collection points into a hub, for consolidation before transporting to onward destinations. Decisions are made in advance by cargo forwarders as to the containers to book, both in regions and in the hub, since airlines offer discounts on containers booked in advance; however, cargo quantities are uncertain when advance bookings are made. We develop a two-stage stochastic programming model, where the first stage determines both the quantities and types of air containers to book; the second stage deals with ordering any extra containers, at higher cost, or returning unused containers, as well as making loading and consolidation plans. The objective is to minimise the total expected costs. We then extend it into a multistage case and design a genetic algorithm as the solution method. Experimental results demonstrate that the proposed approaches provide a cost-efficient plan and responsive to demand as it arises.

Publication DOI: https://doi.org/10.1080/01605682.2022.2096493
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Aston University (General)
Additional Information: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publication ISSN: 1476-9360
Last Modified: 02 Dec 2024 08:41
Date Deposited: 22 Jun 2022 11:17
Full Text Link:
Related URLs: https://www.tan ... 82.2022.2096493 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-07-18
Published Online Date: 2022-07-18
Accepted Date: 2022-06-18
Authors: Zhu, Lin
Wu, Yue
Smith, Honora
Luo, Jiabin (ORCID Profile 0000-0002-2599-2822)

Download

[img]

Version: Accepted Version

Access Restriction: Restricted to Repository staff only


[img]

Version: Published Version

Access Restriction: Restricted to Repository staff only

License: Creative Commons Attribution Non-commercial No Derivatives


[img]

Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Statistics

Additional statistics for this record