Table of Contents Author Guidelines Submit a Manuscript
Abstract and Applied Analysis
Volume 2014, Article ID 164961, 10 pages
http://dx.doi.org/10.1155/2014/164961
Research Article

Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

College of Field Engineering, PLA University of Science and Technology, Nanjing 210007, China

Received 22 January 2014; Revised 5 June 2014; Accepted 13 July 2014; Published 23 July 2014

Academic Editor: Mohammad T. Darvishi

Copyright © 2014 Huaixiao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Brucker, R. Qu, and E. Burke, “Personnel scheduling: models and complexity,” European Journal of Operational Research, vol. 210, no. 3, pp. 467–473, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  2. T. Lapègue, O. Bellenguez-Morineau, and D. Prot, “A constraint-based approach for the shift design personnel task scheduling problem with equity,” Computers and Operations Research, vol. 40, no. 10, pp. 2450–2465, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. S.-W. Lin and K.-C. Ying, “Minimizing shifts for personnel task scheduling problems: a three-phase algorithm,” European Journal of Operational Research, vol. 237, no. 1, pp. 323–334, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  4. M. Krishnamoorthy, A. T. Ernst, and D. Baatar, “Algorithms for large scale shift minimisation personnel task scheduling problems,” European Journal of Operational Research, vol. 219, no. 1, pp. 34–48, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  5. G. DuCote and E. M. Malstrom, “Design of personnel scheduling software for manufacturing,” Computers and Industrial Engineering, vol. 37, no. 1, pp. 473–476, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. P. M. Koeleman, S. Bhulai, and M. van Meersbergen, “Optimal patient and personnel scheduling policies for care-at-home service facilities,” European Journal of Operational Research, vol. 219, no. 3, pp. 557–563, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. J. van den Bergh, J. Beliën, P. de Bruecker, E. Demeulemeester, and L. de Boeck, “Personnel scheduling: a literature review,” European Journal of Operational Research, vol. 226, no. 3, pp. 367–385, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. M. Sabar, B. Montreuil, and J.-M. Frayret, “A multi-agent-based approach for personnel scheduling in assembly centers,” Engineering Applications of Artificial Intelligence, vol. 22, no. 7, pp. 1080–1088, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. K.-H. Han and J.-H. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the Congress on Evolutionary Computation, pp. 1354–1360, Piscataway, NJ, USA, July 2000. View at Scopus
  10. A. Narayanan and M. Moore, “Quantum-inspired genetic algorithms,” in Proceedings of the IEEE International Conference on Evolutionary Computatio (ICEC '96), pp. 61–66, Nagoya, Japan, May 1996. View at Scopus
  11. H. Wang, J. Liu, J. Zhi, and C. Fu, “The improvement of quantum genetic algorithm and its application on function optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 730749, 10 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Tang, G. Zhang, B. Lin, and B. Zhang, “Hybrid genetic algorithm for flow shop scheduling problem,” in Proceedings of the International Conference on Intelligent Computation Technology and Automation (ICICTA '10), vol. 2, pp. 449–452, Changsha, China, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Zhang, W. Wei, Z. Jiang, and X. Ma, “Research and simulation on flow-shop scheduling problem based on improved genetic algorithm,” in Proceedings of the 7th International Conference on Computer Science and Education (ICCSE '12), pp. 916–919, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. F. Jin and Y. Fu, “Master-slave genetic algorithm for flow shop scheduling with resource flexibility,” in Proceedings of the IEEE International Conference on Advanced Management Science (ICAMS '10), vol. 1, pp. 341–346, Chengdu, China, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Zhang and H. Rong, “Quantum-inspired genetic algorithm based time-frequency atom decomposition,” in Proceedings of the 7th international conference on Computational Science (ICCS '07), 2007.
  16. J.-A. Yang and Z.-Q. Zhuang, “Actuality of research on quantum genetic algorithm,” Journal of Computer Science & Technology, vol. 30, no. 11, pp. 13–15, 2003. View at Google Scholar
  17. W. Shu and B. He, “A quantum genetic simulated annealing algorithm for task scheduling,” in Advances in Computation and Intelligence, L. Kang, Y. Liu, and S. Zeng, Eds., vol. 4683 of Lecture Notes in Computer Science, pp. 169–176, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  18. Y. Li, Y. Zhang, Y. Cheng, and X. Jiang, “A novel immune quantum-inspired genetic algorithm,” in Advances in Natural Computation, vol. 3612 of Lecture Notes in Computer Science, pp. 215–218, 2005. View at Publisher · View at Google Scholar
  19. K. Han and J. Kim, “Genetic quantum algorithm and its application to combinatorial optimization problem,” in Proceedings of the Congress on Evolutionary Computation, pp. 1354–1360, San Diego, Calif, USA, July 2000. View at Scopus
  20. H. Miao, H. Wang, and Z. Deng, “Quantum genetic algorithm and its application in power system reactive power optimization,” in Proceedings of the International Conference on Computational Intelligence and Security (CIS '09), vol. 1, pp. 107–111, Beijing, China, 2009. View at Publisher · View at Google Scholar