Beynon, Malcolm James ORCID: https://orcid.org/0000-0002-5757-270X and Wells, Peter Erskine ORCID: https://orcid.org/0000-0002-4376-7178
2010.
Motor vehicle improvement preference ranking: A PROMETHEE and trigonometric differential evolution analysis of their chemical emissions.
Pose, Marcos Gestal and Cebrián, Daniel Rivero, eds.
Soft Computing Methods for Practical Environment Solutions: Techniques and Studies,
Hershey, PA:
IGI Global,
pp. 106-126.
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Abstract
The central theme of this chapter is a preference ranking analysis of a group of motor vehicles based on their chemical emissions. Beyond the initial ranking of the motor vehicles, operationalised using the multi-criteria decision making technique PROMETHEE, further analysis is given on how the manufacturers of a motor vehicle could improve its preference rank position, based on reducing it current levels of chemical emissions. The rank improvement analysis, is defined a constrained optimisation problem, solved here using Trigonometric Differential Evolution. Further, an identification of a prescribed order to these chemical emissions reductions is then identified, offering practical findings to motor vehicle manufacturers when considering their position in a competitive market. The employment of Trigonometric Differential Evolution along with PROMETHEE in this chapter demonstrates a clear example of soft-computing in a practical problem.
| Item Type: | Book Section |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Business (Including Economics) Research Institutes & Centres > Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC) |
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HF Commerce Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Publisher: | IGI Global |
| ISBN: | 9781615208937 |
| Related URLs: | |
| Last Modified: | 05 Feb 2025 18:22 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/24723 |
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