Paper Info
Title
A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem
Abstract
In many real-world applications, processing times may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm based on immune and entropy principle to solve the multi-objective fuzzy FJSP. In this improved multi-objective algorithm, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.
Year
DOI
Venue
2012
10.1504/IJCAT.2012.050700
IJCAT
Keywords
Field
DocType
computational result,practical scheduling problem,genetic algorithm,extended traditional job-shop scheduling,multi-objective fuzzy fjsp,fuzzy flexible job-shop scheduling,proposed algorithm,improved multi-objective algorithm,flexible job-shop scheduling problem,entropy principle,multi-objective genetic algorithm,scheduling problem,job shop scheduling,fuzzy logic
Mathematical optimization,Job shop scheduling,Fair-share scheduling,Premature convergence,Job shop,Flow shop scheduling,Nurse scheduling problem,Genetic algorithm scheduling,Artificial intelligence,Dynamic priority scheduling,Mathematics
Journal
Volume
Issue
ISSN
45
2/3
0952-8091
Citations 
PageRank 
References 
8
0.49
10
Authors
4
Name
Order
Citations
PageRank
Xiao-Juan Wang1228.34
Liang Gao21493128.41
Chaoyong Zhang332023.22
Xinyu Li438165.75