A Quantum-Inspired Genetic Algorithm for Scheduling Problems

  • Ling Wang
  • Hao Wu
  • Da-zhong Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3612)


This paper is the first to propose a quantum-inspired genetic algorithm (QGA) for permutation flow shop scheduling problem to minimize the maximum completion time (makespan). In the QGA, Q-bit based representation is employed for exploration in discrete 0-1 hyperspace by using updating operator of quantum gate as well as genetic operators of Q-bit. Meanwhile, the Q-bit representation is converted to random key representation, which is then transferred to job permutation for objective evaluation. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the QGA, whose searching quality is much better than that of the famous NEH heuristic.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on the Theory of Computing, Pennsylvania, pp. 212–221 (1996)Google Scholar
  2. 2.
    Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings of the 35th Annual Symposium on the Foundation of Computer Sciences, Los Alamitos, pp. 20–22 (1994)Google Scholar
  3. 3.
    Wang, L.: Intelligent Optimization with Applications. Tsinghua University & Springer Press, Beijing (2001)Google Scholar
  4. 4.
    Narayanan, A., Moore, M.: Quantum inspired genetic algorithm. In: IEEE International Conference on Evolutionary Computation, Piscataway, pp. 61–66 (1996)Google Scholar
  5. 5.
    Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evolutionary Computation 6, 580–593 (2002)CrossRefGoogle Scholar
  6. 6.
    Han, K.H., Kim, J.H.: A Quantum-inspired evolutionary algorithms with a new termination criterion, He gate, and two-phase scheme. IEEE Trans. Evol. Comput. 8, 156–169 (2004)CrossRefGoogle Scholar
  7. 7.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: a Guide to the Theory of NP-Completeness. Freeman, San Francisco (1979)MATHGoogle Scholar
  8. 8.
    Nawaz, M., Enscore Jr., E., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11, 91–95 (1983)CrossRefGoogle Scholar
  9. 9.
    Ogbu, F.A., Smith, D.K.: Simulated annealing for the permutation flowshop problem. Omega 19, 64–67 (1990)CrossRefGoogle Scholar
  10. 10.
    Wang, L., Zhang, L., Zheng, D.Z.: A class of order-based genetic algorithm for flow shop scheduling. Int. J. Advanced Manufacture Technology 22, 828–835 (2003)CrossRefGoogle Scholar
  11. 11.
    Wang, L., Zheng, D.Z.: A modified evolutionary programming for flow shop scheduling. Int. J. Advanced Manufacturing Technology 22, 522–527 (2003)CrossRefGoogle Scholar
  12. 12.
    Nowicki, E., Smutnicki, C.: A fast tabu search algorithm for the permutation flow-shop problem. European J. Operational Research 91, 160–175 (1996)MATHCrossRefGoogle Scholar
  13. 13.
    Wang, L., Zheng, D.Z.: An effective hybrid heuristic for flow shop scheduling. Int. J. Advanced Manufacture Technology 21, 38–44 (2003)CrossRefGoogle Scholar
  14. 14.
    Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing 6, 154–160 (1994)MATHGoogle Scholar
  15. 15.
    Carlier, J.: Ordonnancements a contraintes disjonctives. R.A.I.R.O. Recherche operationelle/Operations Research 12, 333–351 (1978)MATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ling Wang
    • 1
  • Hao Wu
    • 1
  • Da-zhong Zheng
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

Personalised recommendations