A Constructive Genetic Algorithm for permutation flowshop scheduling

Authors:
Marcelo Seido Nagano;Rubén Ruiz;Luiz Antonio Nogueira Lorena
Affiliations:
Department of Industrial Engineering, School of Engineering of São Carlos, University of São Paulo, Av. Trabalhador Sãocarlense, 400-Centro, 13566-590 São Carlos, São Paul ...;Department of Applied Statistics and Operations Research, Polytechnic University of Valencia, Camino de Vera S/N, 46021 Valencia, Spain;Computer and Applied Mathematics Laboratory, Brazilian Space Research Institute, Av. dos Astronautas, 1758, 12227-010 São José dos Campos, São Paulo, Brazil
Venue:
Computers and Industrial Engineering
Year:
2008

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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.