Paper Info
Title
Advances in Multiobjective Hybrid Genetic Algorithms for Intelligent Manufacturing and Logistics Systems
Abstract
Recently, genetic algorithms (GA) have received considerable attention regarding their potential as a combinatorial optimization for complex problems and have been successfully applied in the area of various engineering. We will survey recent advances in hybrid genetic algorithms (HGA) with local search and tuning parameters and multiobjective HGA (MO-HGA) with fitness assignments. Applications of HGA and MO-HGA will introduced for flexible job-shop scheduling problem (FJSP), reentrant flow-shop scheduling (RFS) model, and reverse logistics design model in the manufacturing and logistics systems.
Year
DOI
Venue
2013
10.1007/978-3-319-02750-0_41
AMT
Keywords
Field
DocType
reentrant flow-shop scheduling,hybrid genetic algorithms,multiobjective reverse logistics model,multiobjective hga,flexible job-shop scheduling problem
Data mining,Job shop scheduling,Industrial engineering,Reverse logistics,Scheduling (computing),Computer science,Operations research,Combinatorial optimization,Hybrid genetic algorithms,Local search (optimization),Genetic algorithm,Reentrancy
Conference
Volume
ISSN
Citations 
8210
0302-9743
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Mitsuo Gen11873130.43
Kenichi Ida2649.39