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Water Wave Optimization for Flow-Shop Scheduling

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Intelligent Computing Methodologies (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11645))

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Abstract

Flow-shop scheduling problem (FSP) is a well-known combinatorial optimization problem which has a wide range of practical applications. However, FSP is known to be NP-hard when there are more than two machines, for which traditional exact algorithms can only solve small-size problem instances, and many metaheuristic algorithms are mostly suitable for solving large-size instances. Water wave optimization (WWO) is a novel metaheuristic evolutionary algorithm that draws inspiration from shallow water wave model for optimization problems. In this paper, we propose two WWO algorithms for FSP. The first algorithm adapts the original evolutionary operators of the basic WWO according to the solution space of FSP. The second algorithm further improves the first algorithm with a self-adaptive local search procedure. Experimental results on test instances show that the proposed strategies are effective for solving FSP, and the WWO algorithm with self-adaptive local search exhibits significant performance advantages over many other well-known metaheuristic algorithms.

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References

  1. Dasgupta, P., Das, S.: A discrete inter-species cuckoo search for flow-shop scheduling problems. Comput. Oper. Res. 60, 111–120 (2015). https://doi.org/10.1016/j.cor.2015.01.005

    Article  MathSciNet  MATH  Google Scholar 

  2. Du, Y.-C., Zhang, M.-X., Cai, C.-Y., Zheng, Y.-J.: Enhanced biogeography-based optimization for flow-shop scheduling. In: Qiao, J., Zhao, X., Pan, L., Zuo, X., Zhang, X., Zhang, Q., Huang, S. (eds.) BIC-TA 2018. CCIS, vol. 951, pp. 295–306. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-2826-8_26

    Chapter  Google Scholar 

  3. Engin, O., Güçlü, A.: A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems. Appl. Soft Comput. 72, 166–176 (2018). https://doi.org/10.1016/j.asoc.2018.08.002

    Article  Google Scholar 

  4. Etiler, O., Toklu, B., Atak, M., Wilson, J.: A genetic algorithm for flow shop scheduling problems. J. Oper. Res. Soc. 55(8), 830–835 (2004). https://doi.org/10.1057/palgrave.jors.2601766

    Article  MATH  Google Scholar 

  5. Fu, Y., Ding, J., Wang, H., Wang, J.: Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Appl. Soft Comput. 68, 847–855 (2018). https://doi.org/10.1016/j.asoc.2017.12.009

    Article  Google Scholar 

  6. Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1(2), 117–129 (1976)

    Article  MathSciNet  Google Scholar 

  7. Kuo, I.H., et al.: An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model. Expert Syst. Appl. 36(3), 7027–7032 (2009)

    Article  Google Scholar 

  8. Liao, C.J., Tseng, C.T., Luarn, P.: A discrete version of particle swarm optimization for flowshop scheduling problems. Comput. Oper. Res. 34(10), 3099–3111 (2007). https://doi.org/10.1016/j.cor.2005.11.017

    Article  MATH  Google Scholar 

  9. Lin, J.: A hybrid discrete biogeography-based optimization for the permutation flow-shop scheduling problem. Int. J. Prod. Res. 54(16), 4805–4814 (2016). https://doi.org/10.1080/00207543.2015.1094584

    Article  Google Scholar 

  10. Marichelvam, M.K.: An improved hybrid cuckoo search (IHCS) metaheuristics algorithm for permutation flow shop scheduling problems. Int. J. Bio-Inspired Comput. 4(4), 200–205 (2012). https://doi.org/10.1504/IJBIC.2012.048061

    Article  Google Scholar 

  11. Nawaz, M., Enscore, E.E., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11(1), 91–95 (1983). https://doi.org/10.1016/0305-0483(83)90088-9

    Article  Google Scholar 

  12. Onwubolu, G., Davendra, D.: Scheduling flow shops using differential evolution algorithm. Eur. J. Oper. Res. 171(2), 674–692 (2006). https://doi.org/10.1016/j.ejor.2004.08.043

    Article  MATH  Google Scholar 

  13. Pan, Q.K., Tasgetiren, M.F., Liang, Y.C.: A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Comput. Ind. Eng. 55(4), 795–816 (2008). https://doi.org/10.1016/j.cie.2008.03.003

    Article  Google Scholar 

  14. Rajendran, C., Ziegler, H.: Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs. Eur. J. Oper. Res. 155(2), 426–438 (2004). https://doi.org/10.1016/S0377-2217(02)00908-6

    Article  MATH  Google Scholar 

  15. Reeves, C.R.: A genetic algorithm for flowshop sequencing. Comput. Oper. Res. 22(1), 5–13 (1995). https://doi.org/10.1016/0305-0548(93)E0014-K

    Article  MATH  Google Scholar 

  16. Ruiz, R., Vázquez-Rodrı́guez, J.A.: The hybrid flow shop scheduling problem. Eur. J. Oper. Res. 205(1), 1–18 (2010). https://doi.org/10.1016/j.ejor.2009.09.024

    Article  MathSciNet  MATH  Google Scholar 

  17. Santucci, V., Baioletti, M., Milani, A.: Algebraic differential evolution algorithm for the permutation flowshop scheduling problem with total flowtime criterion. IEEE Trans. Evol. Comput. 20(5), 682–694 (2016). https://doi.org/10.1109/TEVC.2015.2507785

    Article  MATH  Google Scholar 

  18. Shao, W., Pi, D., Shao, Z.: An extended teaching-learning based optimization algorithm for solving no-wait flow shop scheduling problem. Appl. Soft Comput. 61, 193–210 (2017). https://doi.org/10.1016/j.asoc.2017.08.020

    Article  Google Scholar 

  19. Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64(2), 278–285 (1993). https://doi.org/10.1016/0377-2217(93)90182-M

    Article  MathSciNet  MATH  Google Scholar 

  20. Wang, L., Zhang, L., Zheng, D.Z.: A class of order-based genetic algorithm for flow shop scheduling. Int. J. Adv. Manuf. Technol. 22(11), 828–835 (2003). https://doi.org/10.1007/s00170-003-1689-8

    Article  Google Scholar 

  21. Zheng, Y.J.: Water wave optimization: a new nature-inspired metaheuristic. Comput. Oper. Res. 55(1), 1–11 (2015). https://doi.org/10.1016/j.cor.2014.10.008

    Article  MathSciNet  MATH  Google Scholar 

  22. Zheng, Y.J., Ling, H.F., Shi, H.H., Chen, H.S., Chen, S.Y.: Emergency railway wagon scheduling by hybrid biogeography-based optimization. Comput. Oper. Res. 43(3), 1–8 (2014). https://doi.org/10.1016/j.cor.2013.09.002

    Article  MathSciNet  MATH  Google Scholar 

  23. Zheng, Y.J., Zhang, B.: A simplified water wave optimization algorithm. In: IEEE Congress on Evolutionary Computation, pp. 807–813 (2015).https://doi.org/10.1109/CEC.2015.7256974

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Jia-Yu Wu, Xue Wu, Xue-Qin Lu, Yi-Chen Du & Min-Xia Zhang

Authors
  1. Jia-Yu Wu
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  2. Xue Wu
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  3. Xue-Qin Lu
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  4. Yi-Chen Du
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  5. Min-Xia Zhang
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Corresponding author

Correspondence to Min-Xia Zhang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

  3. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Wu, JY., Wu, X., Lu, XQ., Du, YC., Zhang, MX. (2019). Water Wave Optimization for Flow-Shop Scheduling. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_70

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  • DOI: https://doi.org/10.1007/978-3-030-26766-7_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26765-0

  • Online ISBN: 978-3-030-26766-7

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