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Motor vehicle improvement preference ranking: A PROMETHEE and trigonometric differential evolution analysis of their chemical emissions

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: Business (Including Economics)
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: 19 Oct 2022 10:32
URI: https://orca.cardiff.ac.uk/id/eprint/24723

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