Annals of Operations Research

, Volume 180, Issue 1, pp 197–211

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

Authors

    • Department of Information ManagementYuan-Ze University
  • Shih-Hsin Chen
    • Department of Electronic Commerce ManagementNanhua University
  • Chin-Yuan Fan
    • Department of Industrial Engineering and ManagementYuan-Ze University
  • V. Mani
    • Department of Aerospace EngineeringIndian Institute of Science
Article

DOI: 10.1007/s10479-008-0489-9

Cite this article as:
Chang, P., Chen, S., Fan, C. et al. Ann Oper Res (2010) 180: 197. doi:10.1007/s10479-008-0489-9

Abstract

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the “evaporation concept” applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The “evaporation concept” is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

Keywords

Evolutionary algorithm with probabilistic modelsSingle machine schedulingTotal deviationsFlowshop machine schedulingArtificial chromosomes

Copyright information

© Springer Science+Business Media, LLC 2008