Paper Info
Title
Multi-objective fuzzy assembly line balancing using genetic algorithms
Abstract
This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 (SALBP-2) with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The jobs processing times are formulated by triangular fuzzy membership functions. The total fuzzy cost function is formulated as the weighted-sum of two bi-criteria fuzzy objectives: (a) Minimizing the fuzzy cycle time and the fuzzy smoothness index of the workload of the line. (b) Minimizing the fuzzy cycle time of the line and the fuzzy balance delay time of the workstations. A new multi-objective genetic algorithm is applied to solve the problem whose performance is studied and discussed over known test problems taken from the open literature.
Year
DOI
Venue
2012
10.1007/s10845-010-0400-9
J. Intelligent Manufacturing
Keywords
Field
DocType
Assembly line balancing,Genetic algorithms,Multi-objective optimization,Fuzzy logic,Fuzzy numbers
Mathematical optimization,Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy transportation,Fuzzy number,Fuzzy associative matrix
Journal
Volume
Issue
ISSN
23
3
0956-5515
Citations 
PageRank 
References 
16
0.66
15
Authors
2
Name
Order
Citations
PageRank
P. Th. Zacharia1331.95
Andreas C. Nearchou217314.97