An improved genetic algorithm for the cellular manufacturing system planning

Author

Qi, Chen ; Xiang-Yang, Ma

Author_Institution

Sch. of Inf. Eng., Tianjin Univ. of Commerce, Tianjin, China

fYear

2011

fDate

15-17 April 2011

Firstpage

2954

Lastpage

2957

Abstract

Cellular manufacturing system planning is a combinatorial optimization problem. Because of its nonlinearity, NP-Complete, and other complex characteristics, it can not be easily solved by conventional methods. Development of modern computing technology provides the possibility. This paper introduces the grouping efficiency and performance indicators to measure the merits of system planning. By focusing on the fuzzy nature of processing paths exised in either the whole machine or in parts, an improved genetic algorithm based on fuzzy theory is proposed to achieve cellular manufacturing system planning. This algorithm uses the fuzzy relation matrix coding of chromosomes to narrow the search space. Simulation results showed the effectiveness of the designed algorithm.

Keywords

cellular manufacturing; combinatorial mathematics; fuzzy set theory; genetic algorithms; NP-complete characteristics; cellular manufacturing system planning; combinatorial optimization problem; computing technology; fuzzy relation matrix coding; fuzzy theory; grouping efficiency; improved genetic algorithm; processing paths; Algorithm design and analysis; Cellular manufacturing; Computers; Genetic algorithms; Job shop scheduling; Planning; cellular manufacturing; fuzzy theory; genetic algorithm; system programming;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-8036-4

Type

conf

DOI

10.1109/ICEICE.2011.5778356

Filename

5778356