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Mathematical Problems in Engineering
Volume 2013, Article ID 976065, 7 pages
http://dx.doi.org/10.1155/2013/976065
Research Article

Inventory Based Bi-Objective Flow Shop Scheduling Model and Its Hybrid Genetic Algorithm

School of Science, School of Computer Science and Technology, Xidian University, Xi’an 710071, China

Received 31 August 2012; Accepted 5 March 2013

Academic Editor: Alexander Pogromsky

Copyright © 2013 Ren Qing-dao-er-ji and Yuping Wang. 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. S. M. Johnson, “Optimal two-and three-stage production schedules with setup times included,” Naval Research Logistics Quarterly, vol. 1, no. 1, pp. 61–68, 1954. View at Google Scholar
  2. J. K. Lenstra, A. H. G. R. Kan, and P. Brucker, “Complexity of machine scheduling problems,” Annals of Discrete Mathematics, vol. 1, pp. 343–362, 1977. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Hege, M. Minoux, and W. van Canneyt, “A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints,” Computers & Operations Research, vol. 39, no. 3, pp. 629–636, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  4. K. Gokbayrak and O. Selvi, “Service time optimization of mixed-line flow shop systems,” IEEE Transactions on Automatic Control, vol. 55, no. 2, pp. 395–404, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. D. Ravindran, S. J. Selvakumar, R. Sivaraman, and A. N. Haq, “Flow shop scheduling with multiple objective of minimizing makespan and total flow time,” International Journal of Advanced Manufacturing Technology, vol. 25, no. 9, pp. 1007–1012, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Eren and E. Güner, “A bicriteria scheduling with sequence-dependent setup times,” Applied Mathematics and Computation, vol. 179, no. 1, pp. 378–385, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  7. J. Lemesre, C. Dhaenens, and E. G. Talbi, “An exact parallel method for a bi-objective permutation flowshop problem,” European Journal of Operational Research, vol. 177, no. 3, pp. 1641–1655, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  8. C. T. Tseng and C. J. Liao, “A discrete particle swarm optimization for lot-streaming flowshop scheduling problem,” European Journal of Operational Research, vol. 191, no. 2, pp. 360–373, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Naderi, M. Zandieh, A. Khaleghi Ghoshe Balagh, and V. Roshanaei, “An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness,” Expert Systems with Applications, vol. 36, no. 6, pp. 9625–9633, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. B. Qian, L. Wang, D. X. Huang, and X. Wang, “Multi-objective flow shop scheduling using differential evolution,” Lecture Notes in Control and Information Sciences, vol. 345, pp. 1125–1136, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Pasupathy, C. Rajendran, and R. K. Suresh, “A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs,” International Journal of Advanced Manufacturing Technology, vol. 27, no. 7-8, pp. 804–815, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Melab, M. Mezmaz, and E. G. Talbi, “Parallel cooperative meta-heuristics on the computational grid. A case study: the bi-objective Flow-Shop problem,” Parallel Computing, vol. 32, no. 9, pp. 643–659, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. R. Tavakkoli-Moghaddam, A. Rahimi-Vahed, and A. H. Mirzaei, “A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: weighted mean completion time and weighted mean tardiness,” Information Sciences, vol. 177, no. 22, pp. 5072–5090, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. B. B. Li and L. Wang, “A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 37, no. 3, pp. 576–591, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. P. C. Chang, S. H. Chen, and C. H. Liu, “Sub-population genetic algorithm with mining gene structures for multiobjective flowshop scheduling problems,” Expert Systems with Applications, vol. 33, no. 3, pp. 762–771, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. F. Dugardin, F. Yalaoui, and L. Amodeo, “New multi-objective method to solve reentrant hybrid flow shop scheduling problem,” European Journal of Operational Research, vol. 203, no. 1, pp. 22–31, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  17. N. Karimi, M. Zandieh, and H. R. Karamooz, “Bi-objective group scheduling in hybrid flexible flowshop: a multi-phase approach,” Expert Systems with Applications, vol. 37, no. 6, pp. 4024–4032, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M. J. Geiger, “Decision support for multi-objective flow shop scheduling by the Pareto Iterated Local Search methodology,” Computers & Industrial Engineering, vol. 61, no. 3, pp. 805–812, 2011. View at Google Scholar
  19. T. C. Chiang, H. C. Cheng, and L. C. Fu, “NNMA: an effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems,” Expert Systems with Applications, vol. 38, no. 5, pp. 5986–5999, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Dubois-Lacoste, M. López-Ibáñez, and T. Stützle, “A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems,” Computers & Operations Research, vol. 38, no. 8, pp. 1219–1236, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  21. H. M. Cho, S. J. Bae, J. Kim, and I. J. Jeong, “Bi-objective scheduling for reentrant hybrid flow shop using Pareto genetic algorithm,” Computers & Industrial Engineering, vol. 61, no. 3, pp. 529–541, 2011. View at Google Scholar
  22. D. Lei, “A Pareto archive particle swarm optimization for multi-objective job shop scheduling,” Computers & Industrial Engineering, vol. 54, no. 4, pp. 960–971, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Khalili and R. Tavakkoli-Moghaddam, “A multi-objective electromagnetism algorithm for a bi-objective flow shop scheduling problem,” Journal of Manufacturing Systems, vol. 31, no. 2, pp. 232–239, 2012. View at Google Scholar
  24. J. H. Holland, “Genetic algorithm,” Scientific American, vol. 266, pp. 44–50, 1992. View at Google Scholar
  25. T. Murata, H. Ishibuchi, and H. Tanaka, “Multi-objective genetic algorithm and its applications to flowshop scheduling,” Computers & Industrial Engineering, vol. 30, no. 4, pp. 957–968, 1996. View at Publisher · View at Google Scholar · View at Scopus
  26. R. Qing-dao-er-ji and Y. Wang, “A new hybrid genetic algorithm for job shop scheduling problem,” Computers & Operations Research, vol. 39, no. 10, pp. 2291–2299, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  27. Q. D. E. J. Ren, Y. Wang, and X. Si, “An improved genetic algorithm for job shop scheduling problem,” in Proceedings of the International Conference on Computational Intelligence and Security (CIS '10), pp. 113–116, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Dell'Amico and M. Trubian, “Applying tabu search to the job-shop scheduling problem,” Annals of Operations Research, vol. 41, no. 3, pp. 231–252, 1993. View at Publisher · View at Google Scholar · View at Scopus
  29. C. R. Reeves, “A genetic algorithm for flowshop sequencing,” Computers & Operations Research, vol. 22, no. 1, pp. 5–13, 1995. View at Google Scholar · View at Scopus
  30. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. H. Ishibuchi, T. Yoshida, and T. Murata, “Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 204–223, 2003. View at Publisher · View at Google Scholar · View at Scopus