An adaptive genetic algorithm to the single machine scheduling problem with earliness and tardiness penalties

Authors:
Fábio Fernandes Ribeiro;Marcone Jamilson Freitas Souza;Sérgio Ricardo De Souza
Affiliations:
Centro Federal de Educação Tecnológica de Minas Gerais, Programa de Pós-Graduação em Modelagem Matemática e Computacional, Belo Horizonte, Minas Gerais, Brazil;Universidade Federal de Ouro Preto, Departamento de Computação, ICEB, Ouro Preto, Minas Gerais, Brazil;Centro Federal de Educação Tecnológica de Minas Gerais, Programa de Pós-Graduação em Modelagem Matemática e Computacional, Belo Horizonte, Minas Gerais, Brazil
Venue:
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Year:
2010

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Abstract

This paper deals with the Single Machine Scheduling Problem with Earliness and Tardiness Penalties, considering distinct due windows and sequence-dependent setup time. Due to its complexity, an adaptive genetic algorithm is proposed for solving it. Many search operators are used to explore the solution space where the choice probability for each operator depends on the success in a previous search. The initial population is generated by the combination between construct methods based on greedy, random and GRASP techniques. For each job sequence generated, a polynomial time algorithm is used for determining the processing initial optimal date to each job. During the evaluation process, the best individuals produced are added to a special group, called elite group. The individuals of this group are submitted to refinement, aiming to improve their quality. Three variations of this algorithm are submitted to computational test. The results show the effectiveness of the proposed algorithm.