Paper Info
Title
Interactive Fuzzy Multi-objective Ant Colony Optimization with Linguistically Quantified Decision Functions for Flexible Job Shop Scheduling Problems
Abstract
Scheduling for the flexible job shop is very important in the fields of production management and combinatorial optimization. It proposes an Ant Colony Optimization with Linguistically Quantified Decision Functions (ACO-LQDF) for the Flexible Job Shop Scheduling Problems (FJSSP) in this work. The novelty of the proposed approach is the interactive and fuzzy multi-objective nature of the Ant Colony Optimization (ACO) that considers the aspiration levels set by the decision maker (DM) for the objectives. The ACO's decision function is a linguistically quantified statement about acceptable distances between achieved objective values and aspiration levels. Linguistic quantifiers are represented by means of fuzzy sets. Our computational investigation indicates that this approach can tackle multi-objective FJSSP effectively.
Year
DOI
Venue
2007
10.1109/FBIT.2007.18
FBIT
Keywords
Field
DocType
ant colony optimization,fuzzy set,fuzzy multi-objective nature,multi-objective fjssp,decision function,linguistically quantified decision functions,linguistic quantifiers,aspiration level,decision maker,flexible job shop scheduling,interactive fuzzy multi-objective ant,production management,combinatorial optimization,job shop scheduling,fuzzy set theory
Ant colony optimization algorithms,Mathematical optimization,Job shop scheduling,Computer science,Scheduling (computing),Job shop,Fuzzy logic,Flow shop scheduling,Combinatorial optimization,Fuzzy set
Conference
Citations 
PageRank 
References 
1
0.34
16
Authors
3
Name
Order
Citations
PageRank
Li-ning Xing122921.43
Ying-wu Chen222716.61
Ke-wei Yang319322.65