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Quantum-inspired cuckoo co-search algorithm for no-wait flow shop scheduling

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Abstract

Minimizing the makespan in no-wait flow shop scheduling problem (NWFSP) is widely applied in various industries. However, it is a NP-hard problem. A novel quantum-inspired cuckoo co-search (QCCS) algorithm is proposed to solve this problem. The QCCS algorithm consists of the following three phases: 1) Quantum representation of solution. 2) A quantum-inspired cuckoo search-differential evolution (QCS-DE) search. 3) Local neighborhood search (LNS) algorithm. Meanwhile, the convergence property of the QCCS algorithm is analyzed theoretically. The Taguchi experiments are further designed for the calibration of parameters. The QCCS algorithm was performed on Rec and Car benchmark instances and compared with the state-of-the-art algorithms, including GA-VNS, HGA, TS-PSO, TMIIG, where the superiority of the proposed algorithm is verified by numerical analyses. In addition, the in-depth statistical analysis demonstrates the effectiveness of the proposed algorithm. The numerical results verify that the proposed algorithm has strong optimization ability and can effectively solve the NWFSP with small and medium scale.

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References

  1. Wismer DA (1972) Solution of the flowshop-scheduling problem with no intermediate queues. Oper Res 20:689–697

    Article  MATH  Google Scholar 

  2. Hall NG, Sriskandarajah C (1996) A survey of machine scheduling problems with blocking and no-wait in process. Oper Res 44:510–525

    Article  MathSciNet  MATH  Google Scholar 

  3. Rajendran C (1994) A no-wait flowshop scheduling heuristic to minimize makespan. J Oper Res Soc 45:472–478

    Article  MATH  Google Scholar 

  4. Gilmore PC, Gomory RE (1964) Sequencing a one state-variable machine: a solvable case of the traveling salesman problem. Oper Res 12:655–679

    Article  MathSciNet  MATH  Google Scholar 

  5. Edwin Cheng TC, Wang G, Sriskandarajah C (1999) One-operatorCtwo-machine flowshop scheduling with setup and dismounting times. Comput Oper Res 26:715–730

    Article  MATH  Google Scholar 

  6. Aldowaisan T, Allahverdi A (2004) New heuristics for m-machine no-wait flowshop to minimize total completion time. Omega 32:345–352

    Article  Google Scholar 

  7. Li P, Li S (2008) Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits. Neurocomputing 72:581–591

    Article  Google Scholar 

  8. Ruiz R, Allahverdi A (2009) New heuristics for no-wait flow shops with a linear combination of makespan and maximum lateness. Int J Prod Res 47:5717–5738

    Article  MATH  Google Scholar 

  9. Rabiee M, Zandieh M, Jafarian A (2012) Scheduling of a no-wait two-machine flow shop with sequence-dependent setup times and probable rework using robust meta-heuristics. Int J Prod Res 50:7428–7446

    Article  Google Scholar 

  10. Ramezani P, Rabiee M, Jolai F (2015) No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach. J Intell Manuf 26:731–744

    Article  Google Scholar 

  11. Wang S, Liu M, Chu C (2015) A branch-and-bound algorithm for two-stage no-wait hybrid flow-shop scheduling. Int J Prod Res 53:1143–1167

    Article  Google Scholar 

  12. Lin SW, Ying KC (2015) Optimization of makespan for no-wait flowshop scheduling problems using efficient matheuristics. Omega 64:115–125

    Article  Google Scholar 

  13. Aldowaisan T, Allahverdi A (2012) Minimizing total tardiness in no-wait flowshops. Found Comput Decis Sci 37:149–162

    Article  MathSciNet  MATH  Google Scholar 

  14. Sapkal SU, Laha D (2013) A heuristic for no-wait flow shop scheduling. Int J Adv Manuf Technol 68:1327–1338

    Article  Google Scholar 

  15. Ding JY, Song S, Gupta JND et al (2015) An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Appl Soft Comput 30:604–613

    Article  Google Scholar 

  16. Röck H (1984) The three-machine no-wait flow shop is NP-complete. Journal of the ACM (JACM) 31:336–345

  17. Akrout H et al (2013) New Greedy Randomized Adaptive Search Procedure based on differential evolution algorithm for solving no-wait flowshop scheduling problem. In: International Conference on Advanced Logistics and Transport. IEEE, pp 327–334

  18. Laha D, Gupta JND (2016) A Hungarian penalty-based construction algorithm to minimize makespan and total flow time in no-wait flow shops. Comput Ind Eng 98:373–383

    Article  Google Scholar 

  19. Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput & Applic 24:169–174

    Article  Google Scholar 

  20. Qian B, Wang L, Hu R et al (2009) A DE-based approach to no-wait flow-shop scheduling. Computers & Industrial Engineering 57:787–805

    Article  Google Scholar 

  21. Tseng LY, Lin YT (2010) A hybrid genetic algorithm for no-wait flowshop scheduling problem. Int J Prod Econ 128:144–152

    Article  Google Scholar 

  22. Jarboui B, Eddaly M, Siarry P (2011) A hybrid genetic algorithm for solving no-wait flowshop scheduling problems. Int J Adv Manuf Technol 54:1129–1143

    Article  Google Scholar 

  23. Samarghandi H, ElMekkawy TY (2012) A meta-heuristic approach for solving the no-wait flow-shop problem. Int J Prod Res 50:1–14

    Article  Google Scholar 

  24. Davendra D, Zelinka I, Bialic-Davendra M et al (2013) Discrete self-organising migrating algorithm for flow-shop scheduling with no-wait makespan. Math Comput Model 57:100–110

    Article  MathSciNet  MATH  Google Scholar 

  25. Yang Xin She, Deb S (2010) Cuckoo Search via Lvy flights. In: Nature & Biologically Inspired Computing. NaBIC 2009. World Congress on IEEE, pp 210–214

  26. Han KH, Kim JH (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6:580–593

    Article  MathSciNet  Google Scholar 

  27. Nezamabadi-pour H (2015) A quantum-inspired gravitational search algorithm for binary encoded optimization problems. Eng Appl Artif Intell 40:62–75

    Article  Google Scholar 

  28. Draa A, Meshoul S, Talbi H et al (2011) A quantum-inspired differential evolution algorithm for solving the N-queens problem. Neural Netw 1:12

    Google Scholar 

  29. Carlier Jacques (2011) Ordonnancements contraintes disjonctives. RAIRO - Operations Research 12:333–350

    Article  MathSciNet  MATH  Google Scholar 

  30. Reeves C (1995) A genetic algorithm for flowshop sequencing. Computers & operations research 22:5–13

    Article  MATH  Google Scholar 

  31. Taillard E (1993) Benchmarks for basic scheduling programs. Eur J Oper Res 64:278–285

    Article  MATH  Google Scholar 

  32. Zheng T, Yamashiro M (2010) Solving flow shop scheduling problems by quantum differential evolutionary algorithm. Int J Adv Manuf Technol 49:643–662

    Article  Google Scholar 

  33. Li P, Li S (2008) Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits. Neurocomputing 72:581–591

    Article  Google Scholar 

  34. Framinan JM, Leisten R (2003) An efficient constructive heuristic for flowtime minimisation in permutation flow shops. Omega 31:311–317

    Article  Google Scholar 

  35. Qi X, Wang H, Zhu H et al (2016) Fast local neighborhood search algorithm for the no-wait flow shop scheduling with total flow time minimization. Int J Prod Res 54:1–16

    Article  Google Scholar 

  36. Ye Honghan, Li W, Miao E (2017) An improved heuristic for no-wait flow shop to minimize makespan. J Manuf Syst 44:273–279

    Article  Google Scholar 

  37. Beyer HG, Schwefel HP (2002) Evolution strategiesCA comprehensive introduction. Nat Comput 1:3–52

    Article  MathSciNet  MATH  Google Scholar 

  38. Montgomery D (2005) Design and analysis of experiments. Technometrics 48:158–158

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the reviewers for their useful comments and suggestions for this paper. This work was supported by the National Natural Science Foundation of China(61672039, 61572036), the University Natural Science Foundation Project of Anhui Province (1808085QF191) and the University Natural Science Research Project of Anhui Province (KJ2016A272).

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Correspondence to Xuemei Qi.

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Zhu, H., Qi, X., Chen, F. et al. Quantum-inspired cuckoo co-search algorithm for no-wait flow shop scheduling. Appl Intell 49, 791–803 (2019). https://doi.org/10.1007/s10489-018-1285-0

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