Paper Info
Title
Modeling and genetic solution of a class of flexible job shop scheduling problems
Abstract
We consider an extended class of flexible job shop scheduling problems. First, we translate the problem into a mathematical programming formula, i.e., a mixed-integer programming problem. This makes it possible to apply standard packages of mixed integer programming solvers and, while lots of computational time is required in general, to obtain the optimal schedule. Then, in order to seek the schedules close to the optimal for larger-scale problems, we newly design a solution method by adopting genetic algorithms based on the formula. Through some computational experiments, the effectiveness and the possibility of the proposed approach are examined.
Year
DOI
Venue
2001
10.1109/ETFA.2001.997705
Emerging Technologies and Factory Automation, 2001. Proceedings. 2001 8th IEEE International Conference
Keywords
Field
DocType
genetic algorithms,mathematical programming,production control,computational experiments,flexible job shop scheduling problems,genetic algorithms,genetic solution,mathematical programming formula,mixed-integer programming problem
Mathematical optimization,Job shop scheduling,Computer science,Flow shop scheduling,Two-level scheduling,Integer programming,Genetic algorithm scheduling,Schedule,Rate-monotonic scheduling,Dynamic priority scheduling
Conference
Volume
ISBN
Citations 
2
0-7803-7241-7
2
PageRank 
References 
Authors
0.42
0
4
Name
Order
Citations
PageRank
Hisashi Tamaki114140.54
Tamami Ono220.42
Hajime Murao3216.70
Kitamura, S.482.82