Open AccessMultiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model
Author(s) -
Farzad Tahriri,
Siti Zawiah Md Dawal,
Zahari Taha
Publication year - 2014
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2014/179085
Subject(s) - assembly line , fuzzy logic , production line , computer science , modular design , mixed model , line (geometry) , mathematical optimization , genetic algorithm , production (economics) , job shop , industrial engineering , mathematics , flow shop scheduling , engineering , artificial intelligence , machine learning , job shop scheduling , routing (electronic design automation) , mechanical engineering , computer network , geometry , economics , macroeconomics , operating system
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
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