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Journal of Applied Mathematics
Volume 2014, Article ID 179085, 19 pages
http://dx.doi.org/10.1155/2014/179085
Research Article

Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model

1Department of Mechanical Engineering, Centre for Product Design and Manufacturing, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
2Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang Darul Makmur, Malaysia

Received 8 November 2013; Revised 20 February 2014; Accepted 11 March 2014; Published 8 May 2014

Academic Editor: Olabisi Falowo

Copyright © 2014 Farzad Tahriri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary goal is to minimize the make-span, setup time, and cost simultaneously in mixed-model assembly lines. Such conflicting goals arise when switching between different products. A genetic algorithm (GA) approach is used to solve this problem, in which trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data.