Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2014
Abstract
Human’s impact on earth through global warming is more or less an accepted fact. Ocean freight is estimated to contribute 4-5% of global carbon emissions and manufacturing companies can aid in reducing this amount. Many companies that ship goods through full container loads do not have the capabilities to ensure the containers they are using minimizes their carbon footprint. One of the reasons is the choice of non-ideal container sizes for their shipments. This paper provides a mathematical model to minimize companies’ shipping carbon footprints by selecting the ideal container sizes appropriate for their shipment volumes. Using data from a selected real-world business case in the manufacturing industry, we show that our model can provide a 13.4% reduction in carbon footprint. We believe that our model is generic for ocean shipment and can be easily adoptable by other manufacturing companies, to be more environmentally sustainable by selecting the appropriate container sizes and reduce the carbon footprint of their ocean freight.
Keywords
carbon emission, carbon footprint, sustainability, data analytics, optimization, ocean freight, MITB student
Discipline
Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
2014 IEEE International Conference on Automation Science and Engineering (CASE): Taipei, Taiwan, 18-22 August: Proceedings
First Page
480
Last Page
485
ISBN
9781479952847
Identifier
10.1109/CoASE.2014.6899369
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
CHONG, Edwin Lik Ming; MA, Nang Laik; and TAN, Kar Way.
Reducing carbon emission of ocean shipments by optimizing container size selection. (2014). 2014 IEEE International Conference on Automation Science and Engineering (CASE): Taipei, Taiwan, 18-22 August: Proceedings. 480-485.
Available at: https://ink.library.smu.edu.sg/sis_research/2443
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/CoASE.2014.6899369
Included in
Computer Sciences Commons, Operations Research, Systems Engineering and Industrial Engineering Commons