Title
A Constructive Genetic Algorithm for permutation flowshop scheduling
Abstract
The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on m machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard's well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling problems.
Year
DOI
Venue
2008
10.1016/j.cie.2007.11.018
Computers & Industrial Engineering
Keywords
Field
DocType
computational experience,general flowshop scheduling problem,flowshop,innovative cga approach,flowshop scheduling,local search heuristic,permutation flowshop scheduling,neh classic heuristic,computational result,constructive genetic algorithm,schemata instantiation,makespan,flowshop scheduling problem,production problem,computer experiment,design of experiment,local search,fitness function,scheduling problem
Population,Mathematical optimization,Heuristic,Job shop scheduling,Scheduling (computing),Computer science,Constructive,Permutation,Local search (optimization),Operations management,Genetic algorithm
Journal
Volume
Issue
ISSN
55
1
Computers & Industrial Engineering
Citations 
PageRank 
References 
20
0.84
17
Authors
5