Advertisement

Tandem Workshop Scheduling Based on Sectional Coding and Varying Length Crossover Genetic Algorithm

  • Hao Sun
  • Xiaojun ZhengEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)

Abstract

For the tandem workshop scheduling problem, the objective of optimization is to obtain minimum total distribution time. To achieve that goal, we propose an optimization model, considering the rated load of automated guided vehicles (AGV) and the different regional transportation speeds. This model has three features. First, the sectional coding rules are adopted because materials need to be transported in batches between machines. Second, the crossover operation with varying length is used because the superior characteristics of the previous generation population could be better passed down to the offspring, thus accelerating the convergence rate of the population. Finally, the mutation operation combining insertion and reverse can maintains the diversity of the population and improve the local search ability of the algorithm. The tandem workshop scheduling problem can apply our algorithm, and the effectiveness of the improvement is demonstrated.

Keywords

Tandem workshop scheduling Genetic algorithm Sectional coding 

Notes

Acknowledgements

This work was supported by the Guidance Program for Natural Science Foundation of Liaoning (No. 20170540138).

References

  1. 1.
    Pen C.T., Du Z.J.: Design of AGV based on AT89S52 MCU. Wireless Internet Technology 13 (2017)Google Scholar
  2. 2.
    Bozer, Y.A., Srinivasan, M.M.: Tandem configuration for automated guided vehicle systems and the analysis of single vehicle loops. IIE Trans. 23, 72–82 (1991)CrossRefGoogle Scholar
  3. 3.
    Zhang, H.: Design and implementation of a hybrid scheduling model for tandem workshop resources in cloud computing environment. CIT 5, 8–11 (2017)Google Scholar
  4. 4.
    Zhou, Q., Liu, J., Wei, F.L.: Single-field tandem workshop scheduling optimization based on genetic taboo search algorithm. I J. Chang. Univ. Sci. Technol. (Nat. Sci.) 4, 32–38 (2014)Google Scholar
  5. 5.
    Bai, S.F., Tang, D.B., Gu, W.B., Zheng, K.: Research of multiple AGV systems based on tandem workshop control module. CNEU 3, 8–12 (2012)Google Scholar
  6. 6.
    Tang, D.B., Lu, X.C., Zheng, K.: Research on tandem AGV scheduling based on neuro-endocrine coordination mechanism. Mach. Build. Autom. 4, 112–115 (2015)Google Scholar
  7. 7.
    Hou, L.Y., Liu, Z.C., Shi, Y.J., Zheng, X.J.: Optimizing machine allocation and loop layout in tandem AGV workshop by the collaborative optimization method. Neural Comput. Appl. 4, 959–974 (2016)Google Scholar
  8. 8.
    Rezapour, S., Zanjirani-Farahani, R., Miandoabchi, E.: A Machine-to-loop assignment and layout design methodology for tandem AGV systems with single-load vehicles. Int. J. Prod. Res. 49, 3605–3633 (2011)CrossRefGoogle Scholar
  9. 9.
    Chen, Z.T.: Research and application on job shop scheduling problem based on improved genetic algorithm, p. 32. Dalian University of Technology, DalianGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringDalian Jiaotong UniversityDalianChina

Personalised recommendations