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Improved Shuffled Frog Leaping Algorithm for Solving Multi-aisle Automated Warehouse Scheduling Optimization

  • Wenqiang Yang
  • Li Deng
  • Qun Niu
  • Minrui Fei
Part of the Communications in Computer and Information Science book series (CCIS, volume 402)

Abstract

For multi-aisle automated warehouse scheduling optimization problem, a mathematical model with constraints is established, and a new shuffled frog leaping algorithm is proposed. During the process of obtaining optimal solution, to enhance the local search ability, stepsize is adjusted adaptively, and the frog individuals are guided to update. Meanwhile, in order to maintain the diversity of the populations and strengthen the global search ability, heuristic mutation operation is embedded. This not only ensures the global optimization, but also enhances the convergence efficiency. To verify the performance of the proposed algorithm, it is compared with shuffled frog leaping algorithm (SFLA) and genetic algorithm (GA) through simulation combined the industrial real case. Results show that the proposed algorithm achieves good performance in terms of the solution quality and the convergence efficiency.

Keywords

shuffled frog leaping algorithm adaptive stepsize diversity of the population guided update multi-aisle automated warehouse scheduling optimization 

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References

  1. 1.
    Gagliardi, J.P., Renaud, J., Ruiz, A.: Models for automated storage and retrieval systems: a literature review. International Journal of Production Research 50(24), 7110–7125 (2012)CrossRefGoogle Scholar
  2. 2.
    Atoum, J., Rababaa, M.A.: Multiple Warehouses Scheduling Using Steady State Genetic Algorithms. The International Arab Journal of Information Technology 7(3), 310–316 (2010)Google Scholar
  3. 3.
    Yu, M.F., Koster, R.D.: Enhancing performance in order picking processes by dynamic storage systems. International Journal of Production Research (48), 4785-4806 (2010)Google Scholar
  4. 4.
    Nishi, T., Konishi, M.: An optimisation model and its effective beam search heuristics for floor-storage warehousing systems. International Journal of Production Research (48), 1947-1966 (2010)Google Scholar
  5. 5.
    Chan, F.T.S., Kumar, V.: Hybrid TSSA algorithm-based approach to solve warehouse-scheduling problems. International Journal of Production Research 47(4), 919–940 (2009)CrossRefzbMATHGoogle Scholar
  6. 6.
    De Koster, R.B.M., Le Duc, T., Yugang, Y.: Optimal storage rack design for a 3-dimensional compact AS/RS. International Journal of Production Research 46(6), 1495–1514 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Eusuff, M., Lansey, K.: Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management 129(3), 210–225 (2003)CrossRefGoogle Scholar
  8. 8.
    Gao, H.Y., Cao, J.L.: Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation. Journal of Systems Engineering and Electronics 23(5), 679–688 (2012)Google Scholar
  9. 9.
    Bijami, E., Askari, J., Farsangi, M.M.: Design of stabilising signals for power system damping using generalised predictive control optimised by a new hybrid shuffled frog leaping algorithm. IET Generation, Transmission and Distribution 6(10), 1036–1045 (2012)CrossRefGoogle Scholar
  10. 10.
    Wang, N., Li, X., Chen, X.H.: Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm. Pattern Recognition Letters 31(13), 1809–1815 (2010)CrossRefGoogle Scholar
  11. 11.
    Jafari, A., Bijami, E., Bana, H.R., et al.: A design automation system for CMOS analog integrated circuits using New Hybrid Shuffled Frog Leaping Algorithm. Microelectronics Journal 43(11), 908–915 (2012)CrossRefGoogle Scholar
  12. 12.
    Park, B., Lee, J.: Optimization of coordinated-actuated traffic signal system: Stochastic optimization method based on shuffled frog-leaping algorithm. Transportation Research Record (2128), 76-85 (2009)Google Scholar
  13. 13.
    Pakravesh, H., Shojaei, A.: Optimization of industrial CSTR for vinyl acetate polymerization using novel shuffled frog leaping based hybrid algorithms and dynamic modeling. Computers and Chemical Engineering 35(11), 2351–2365 (2011)CrossRefGoogle Scholar
  14. 14.
    Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management 129(3), 210–225 (2003)CrossRefGoogle Scholar
  15. 15.
    Hsu, C.M., Chen, K.Y., Chen, M.C.: Batching orders in warehouses by minimizing travel distance with genetic algorithms. Computers in Industry 56(2), 169–178 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wenqiang Yang
    • 1
  • Li Deng
    • 1
  • Qun Niu
    • 1
  • Minrui Fei
    • 1
  1. 1.Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina

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