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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 634812, 17 pages
http://dx.doi.org/10.1155/2013/634812
Research Article

Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms

1Department of Information Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan
2Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 106, Taiwan
3Taiwan Information Security Center, National Taiwan University of Science and Technology, Taipei 106, Taiwan

Received 21 January 2013; Revised 7 May 2013; Accepted 27 May 2013

Academic Editor: Jein-Shan Chen

Copyright © 2013 Kuo-Yang Wu 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. O. V. K. Chetty and M. S. Reddy, “Genetic algorithms for studies on AS/RS integrated with machines,” International Journal of Advanced Manufacturing Technology, vol. 22, no. 11-12, pp. 932–940, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. W. Wang, W. Fu, M. Mingyun, D. Li, and S. Yang, “Selection of AS/RS scheduling rules based on genetic algorithm,” in Proceedings of the IEEE International Conference on Automation and Logistics (ICAL '07), pp. 536–540, Jinan, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Asokan, J. Jerald, S. Arunachalam, and T. Page, “Application of adaptive genetic algorithm and particle swarm optimisation in scheduling of jobs and AS/RS in FMS,” International Journal of Manufacturing Research, vol. 3, no. 4, pp. 393–405, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. Q. Tang and F. Xie, “An approach for picking optimization in automated warehouse,” in Proceedings of the 5th International Conference on Natural Computation (ICNC '09), pp. 362–366, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Liu, Y. Huang, and P. Ding, “Research of compound operation optimization based on partheno genetic algorithm,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI '10), pp. 2945–2948, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Huang, W. Liu, P. Ding, and H. Liu, “Research on the compound operation optimization problem of AS/RS,” in Proceedings of the IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, pp. 1130–1133, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. W. Yan, “The study on the simulation of a distribution center operating system and optimization of equipment configuration,” in Proceedings of the Chinese Control and Decision Conference, pp. 3452–3456, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Zhou and L. Mao, “Design and simulation of storage location optimization module in AS/RS based on FLEXSIM,” International Journal of Intelligent Systems and Applications, vol. 2, pp. 33–40, 2010.
  9. M. A. Tawhid, “Nonsmooth generalized complementarity as unconstrained optimization,” Journal of Industrial and Management Optimization, vol. 6, no. 2, pp. 411–423, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  10. J. Zhou, N. Xiu, and J.-S. Chen, “Solution properties and error bounds for semi-infinite complementarity problems,” Journal of Industrial and Management Optimization, vol. 9, no. 1, pp. 99–115, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  11. J. Zhou, J.-S. Chen, and G. M. Lee, “On set-valued complementarity problems,” Abstract and Applied Analysis, vol. 2013, Article ID 105930, 11 pages, 2013. View at Publisher · View at Google Scholar
  12. S. Pan, S. Bi, and J. S. Chen, “Nonsingularity conditions for FB system of reformulating nonlinear second-order cone programming,” Abstract and Applied Analysis, vol. 2013, Article ID 602735, 21 pages, 2013. View at Publisher · View at Google Scholar
  13. B. Xing, W.-J. Gao, F. V. Nelwamondo, K. Battle, and T. Marwala, “Storage and retrieval machine travel route planning strategy in an automated material handling environment,” in Proceedings of the World Automation Congress, September 2010. View at Scopus
  14. W. Yang, X. Qiu, and H. Wang, “The research of hybrid scheduling model in AS/RS based on multi-agent system and Petri Net,” in Proceedings of the International Conference of Information Science and Management Engineering, pp. 120–124, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Zheng, W. Liu, C. Lu, H. Wang, and C. Song, “Modeling and optimizing research for the steel tube AS/RS system,” in Proceedings of the Chinese Control and Decision Conference, pp. 3979–3983, Mianyang, China, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Huang and S. Fu, “Research on movement control technology of Stacking Crane,” in Proceedings of the International Conference on Electric Information and Control Engineering, pp. 5254–5256, April 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Rajkumar, P. Asokan, N. Anilkumar, and T. Page, “A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints,” International Journal of Production Research, vol. 49, no. 8, pp. 2409–2423, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. O. Casdin, P. Castagna, Z. Sari, and N. Meghelli, “Performance evaluation of in-deep class storage for flow-rack AS/RS,” International Journal of Production Research, vol. 50, pp. 22–24, 2012.
  19. http://www.econorack.com/double_deep.
  20. J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975. View at MathSciNet
  21. M. V. Raj, S. S. Sankar, and S. G. Ponnambalam, “Genetic algorithm to optimize manufacturing system efficiency in batch selective assembly,” International Journal of Advanced Manufacturing Technology, vol. 57, no. 5–8, pp. 795–810, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Abbasi and M. Houshmand, “Production planning and performance optimization of reconfigurable manufacturing systems using genetic algorithm,” International Journal of Advanced Manufacturing Technology, vol. 54, no. 1–4, pp. 373–392, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Wang and Y. Koren, “Scalability planning for reconfigurable manufacturing systems,” Journal of Manufacturing Systems, vol. 31, no. 2, pp. 83–91, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. K. Xing, L. Han, M. Zhou, and F. Wang, “Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 42, no. 3, pp. 603–615, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. O. A. Arqub, Z. Abo-Hammour, S. Momani, and N. Shawagfeh, “Solving singular two-point boundary value problems using continuous genetic algorithm,” Abstract and Applied Analysis, vol. 2012, Article ID 205391, 25 pages, 2012. View at Publisher · View at Google Scholar
  26. N. K. Jerne, “The immune system,” Scientific American, vol. 229, no. 1, pp. 52–60, 1973. View at Scopus
  27. J. D. Farmer, N. H. Packard, and A. S. Perelson, “The immune system, adaptation, and machine learning,” Physica D, vol. 22, no. 1–3, pp. 187–204, 1986. View at MathSciNet · View at Scopus
  28. L. Jiao and L. Wang, “A novel genetic algorithm based on immunity,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 30, no. 5, pp. 552–561, 2000. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Timmis and L. N. D. Castro, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, 2002.
  30. S. Yang, “A comparative study of immune system based genetic algorithms in dynamic environments,” in Proceedings of the 8th Annual Genetic and Evolutionary Computation Conference, pp. 1377–1384, July 2006. View at Scopus
  31. L. Yan and K. Yang, “Immunity genetic algorithm based on elitist strategy and its application to the TSP problem,” in Proceedings of the International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, vol. 2, pp. 729–732, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. T.-C. Chen and Y.-C. Hsieh, “Using immune-based genetic algorithms for single trader's periodic marketing problem,” Mathematical and Computer Modelling, vol. 48, no. 3-4, pp. 420–428, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995. View at Scopus
  34. F. Tao, D. Zhao, Y. Hu, and Z. Zhou, “Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system,” IEEE Transactions on Industrial Informatics, vol. 4, no. 4, pp. 315–327, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. M. V. Raj, S. S. Sankar, and S. G. Ponnambalam, “Particle swarm optimization algorithm to maximize assembly efficiency,” International Journal of Advanced Manufacturing Technology, vol. 59, no. 5–8, pp. 719–736, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. X. Li, L. Gao, and X. Wen, “Application of an efficient modified particle swarm optimization algorithm for process planning,” International Journal of Advanced Manufacturing Technology, 2012. View at Publisher · View at Google Scholar
  37. T. Zhang, T. Hu, J.-W. Chen, Z. Wan, and X. Guo, “Solving bilevel multiobjective programming problem by elite quantum behaved particle swarm optimization,” Abstract and Applied Analysis, vol. 2012, Article ID 102482, 20 pages, 2012. View at Zentralblatt MATH · View at MathSciNet
  38. V. Roberge, M. Tarbouchi, and G. Labonté, “Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 132–141, 2013.