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

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  • Pei-Chann Chang
  • Shih-Hsin Chen
  • Chin-Yuan Fan
  • V. Mani

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. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Pei-Chann Chang & Shih-Hsin Chen & Chin-Yuan Fan & V. Mani, 2010. "Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems," Annals of Operations Research, Springer, vol. 180(1), pages 197-211, November.
    • Handle: RePEc:spr:annopr:v:180:y:2010:i:1:p:197-211:10.1007/s10479-008-0489-9
    DOI: 10.1007/s10479-008-0489-9
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    Cited by:

    1. Chang, Pei-Chann & Huang, Wei-Hsiu & Wu, Jheng-Long & Cheng, T.C.E., 2013. "A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 45-55.
    2. Chen, Shih-Hsin & Chen, Min-Chih, 2013. "Addressing the advantages of using ensemble probabilistic models in Estimation of Distribution Algorithms for scheduling problems," International Journal of Production Economics, Elsevier, vol. 141(1), pages 24-33.
    3. Fernandez-Viagas, Victor & Ruiz, Rubén & Framinan, Jose M., 2017. "A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 707-721.

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