 The multi-objective optimisation design for a light guide rodYu-Sen Yang, Chun-Yao Shih, Hong-Yao Chien and Rong-Fong Fung International Journal of Industrial and Systems Engineering, 2015, vol. 20, issue 2, 165-179 Abstract: The light guide rod (LGR) has been popularly used for the vehicles, and the automobile lamp industries need mass production to match this trend. This paper aims to develop a systemic way to find the best parameters' combination for the LGR, and the parameters are usually restricted (existing October 11, 2012) to some levels and random values. In this paper, the LGR example with two optical performances of illuminance flux and uniformity is to be optimised by use of the real-coded genetic algorithm (RGA) and grey relational analysis (GRA). The illuminance flux and uniformity of the best parameters' combination are obtained and compared with the initial set. Comparisons with Taguchi-Grey can improve 5% of gain and comparisons with Pareto genetic algorithm (PaGA) can improve 1.7% of gain. The combined multi-objective optimisation can save 7% time and it is found that the new proposed method has positive gains in performances. Keywords: grey relational analysis; GRA; light guide rods; LGR design; multi-objective optimisation; real-coded genetic algorithms; RGA; automotive lamps; automotive lighting; automobile industry. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:20:y:2015:i:2:p:165-179 Access Statistics for this article More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd |
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