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The Scientific World Journal
Volume 2014, Article ID 596850, 11 pages
http://dx.doi.org/10.1155/2014/596850
Research Article

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

1State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, China
2College of Computer Science, Liaocheng University, Liaocheng 252059, China

Received 21 February 2014; Accepted 30 March 2014; Published 29 April 2014

Academic Editors: P. Agarwal, V. Bhatnagar, and Y. Zhang

Copyright © 2014 Jun-qing Li 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. A. Costa, F. A. Cappadonna, and S. Fichera, “A dual encoding-based meta-heuristic algorithm for solving a constrained hybrid flow shop scheduling problem,” Computers and Industrial Engineering, vol. 64, no. 4, pp. 937–958, 2013. View at Google Scholar
  2. E. Figielska, “A heuristic for scheduling in a two-stage hybrid flowshop with renewable resources shared among the stages,” European Journal of Operational Research, vol. 236, no. 2, pp. 433–444, 201. View at Google Scholar
  3. K. Mao, Q. K. Pan, X. Pang, and T. Chai, “A novel Lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process,” European Journal of Operational Research, vol. 236, no. 1, pp. 51–60, 2014. View at Publisher · View at Google Scholar
  4. H. Luo, A. Zhang, and G. Q. Huang, “Active scheduling for hybrid flowshop with family setup time and inconsistent family formation,” Journal of Intelligent Manufacturing, 2013. View at Publisher · View at Google Scholar
  5. L. C. Wang, Y. Y. Chen, T. L. Chen, C. Y. Cheng, and C. W. Chang, “A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry,” International Journal of Systems Science, 2013. View at Publisher · View at Google Scholar
  6. T. P. Chung and C. J. Liao, “An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem,” Applied Soft Computing, vol. 13, no. 8, pp. 3729–3736, 2013. View at Google Scholar
  7. M. Abbas Bozorgirad and R. Logendran, “Bi-criteria group scheduling in hybrid flowshops,” International Journal of Production Economics, vol. 145, no. 2, pp. 599–612, 2013. View at Google Scholar
  8. F. D. Chou, “Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks,” International Journal of Production Economics, vol. 141, no. 1, pp. 137–145, 2013. View at Google Scholar
  9. J. Yang, “A two-stage hybrid flow shop with dedicated machines at the first stage,” Computers and Operations Research, vol. 40, no. 12, pp. 2836–2843, 2013. View at Google Scholar
  10. P. Ramezani, M. Rabiee, and F. Jolai, “No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach,” Journal of Intelligent Manufacturing. View at Publisher · View at Google Scholar
  11. T. H. Tran and K. M. Ng, “A hybrid water flow algorithm for multi-objective flexible flow shop scheduling problems,” Engineering Optimization, vol. 45, no. 4, pp. 483–502, 2013. View at Google Scholar
  12. H. Allaoui and A. Artiba, “Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints,” Computers and Industrial Engineering, vol. 47, no. 4, pp. 431–450, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Xie and X. Wang, “Complexity and algorithms for two-stage flexible flowshop scheduling with availability constraints,” Computers and Mathematics with Applications, vol. 50, no. 10–12, pp. 1629–1638, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Allaoui and A. Artiba, “Scheduling two-stage hybrid flow shop with availability constraints,” Computers and Operations Research, vol. 33, no. 5, pp. 1399–1419, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Ruiz, J. Carlos García-Díaz, and C. Maroto, “Considering scheduling and preventive maintenance in the flowshop sequencing problem,” Computers and Operations Research, vol. 34, no. 11, pp. 3314–3330, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Naderi, M. Zandieh, and S. M. T. Fatemi Ghomi, “A study on integrating sequence dependent setup time flexible flow lines and preventive maintenance scheduling,” Journal of Intelligent Manufacturing, vol. 20, no. 6, pp. 683–694, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Berrichi, L. Amodeo, F. Yalaoui, E. Châtelet, and M. Mezghiche, “Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem,” Journal of Intelligent Manufacturing, vol. 20, no. 4, pp. 389–400, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. H. Luo, G. Q. Huang, Y. Zhang, Q. Dai, and X. Chen, “Two-stage hybrid batching flowshop scheduling with blocking and machine availability constraints using genetic algorithm,” Robotics and Computer-Integrated Manufacturing, vol. 25, no. 6, pp. 962–971, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Allaoui and A. Artiba, “Johnson's algorithm: a key to solve optimally or approximately flow shop scheduling problems with unavailability periods,” International Journal of Production Economics, vol. 121, no. 1, pp. 81–87, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. F. Jabbarizadeh, M. Zandieh, and D. Talebi, “Hybrid flexible flowshops with sequence-dependent setup times and machine availability constraints,” Computers and Industrial Engineering, vol. 57, no. 3, pp. 949–957, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Besbes, J. Teghem, and T. Loukil, “Scheduling hybrid flow shop problem with non-fixed availability constraints,” European Journal of Industrial Engineering, vol. 4, no. 4, pp. 413–433, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Ma, C. Chu, and C. Zuo, “A survey of scheduling with deterministic machine availability constraints,” Computers and Industrial Engineering, vol. 58, no. 2, pp. 199–211, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Luo, G. Q. Huang, Y. Feng Zhang, and Q. Yun Dai, “Hybrid flowshop scheduling with batch-discrete processors and machine maintenance in time windows,” International Journal of Production Research, vol. 49, no. 6, pp. 1575–1603, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. E. Safari and S. J. Sadjadi, “A hybrid method for flowshops scheduling with condition-based maintenance constraint and machines breakdown,” Expert Systems with Applications, vol. 38, no. 3, pp. 2020–2029, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Wang and M. Liu, “Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method,” International Journal of Production Research, vol. 52, no. 5, 2014. View at Publisher · View at Google Scholar
  26. M. Rabiee, R. S. Rad, M. Mazinani, and R. Shafaei, “An intelligent hybrid meta-heuristic for solving a case of no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines,” The International Journal of Advanced Manufacturing Technology, vol. 71, no. 5–8, pp. 1229–1245, 2014. View at Publisher · View at Google Scholar
  27. H. Allaoui and A. Artiba, “Hybrid flow shop scheduling with availability constraints,” in Essays in Production, Project Planning and Scheduling, pp. 277–299, Springer, 2014. View at Google Scholar
  28. T. Stützle, “Iterated local search for the quadratic assignment problem,” European Journal of Operational Research, vol. 174, no. 3, pp. 1519–1539, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. H. Hashimoto, M. Yagiura, and T. Ibaraki, “An iterated local search algorithm for the time-dependent vehicle routing problem with time windows,” Discrete Optimization, vol. 5, no. 2, pp. 434–456, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. P. Vansteenwegen, W. Souffriau, G. Vanden Berghe, and D. van Oudheusden, “Iterated local search for the team orienteering problem with time windows,” Computers and Operations Research, vol. 36, no. 12, pp. 3281–3290, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. X. Dong, H. Huang, and P. Chen, “An iterated local search algorithm for the permutation flowshop problem with total flowtime criterion,” Computers and Operations Research, vol. 36, no. 5, pp. 1664–1669, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Piscataway, NJ, USA, December 1995. View at Scopus
  33. B. Liu, L. Wang, and Y.-H. Jin, “An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers,” Computers and Operations Research, vol. 35, no. 9, pp. 2791–2806, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. Q.-K. Pan, M. Fatih Tasgetiren, and Y.-C. Liang, “A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem,” Computers and Operations Research, vol. 35, no. 9, pp. 2807–2839, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. J. Q. Li and Y. X. Pan, “A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem,” The International Journal of Advanced Manufacturing Technology, vol. 66, no. 1–4, pp. 583–593, 2013. View at Google Scholar
  36. C. L. Chen, S. Y. Huang, Y. R. Tzeng, and C. L. Chen, “A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem,” Soft Computing. View at Publisher · View at Google Scholar
  37. Y. Marinakis and M. Marinaki, “Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem,” Soft Computing, vol. 17, no. 7, pp. 1159–1173, 2013. View at Google Scholar
  38. P. Damodaran, A. G. Rao, and S. Mestry, “Particle swarm optimization for scheduling batch processing machines in a permutation flowshop,” International Journal of Advanced Manufacturing Technology, vol. 64, no. 5–8, pp. 989–1000, 2013. View at Publisher · View at Google Scholar · View at Scopus
  39. G. Vijay chakaravarthy, S. Marimuthu, and A. Naveen Sait, “Performance evaluation of proposed differential evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming,” Journal of Intelligent Manufacturing, vol. 24, no. 1, pp. 175–191, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. Y. Chen, C. Y. Cheng, L. C. Wang, and T. L. Chen, “A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems: a case study for solar cell industry,” International Journal of Production Economics, vol. 141, no. 1, pp. 66–78, 2013. View at Google Scholar
  41. S. A. Torabi, N. Sahebjamnia, S. A. Mansouri, and M. A. Bajestani, “A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem,” Applied Soft Computing, vol. 13, no. 12, pp. 4750–4762, 2013. View at Google Scholar
  42. J. Q. Li and Y. X. Pan, “A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem,” The International Journal of Advanced Manufacturing Technology, vol. 66, no. 1–4, pp. 583–596, 20132013. View at Google Scholar
  43. J. T. Tsai, C. I. Yang, and J. H. Chou, “Hybrid sliding level Taguchi-based particle swarm optimization for flowshop scheduling problems,” Applied Soft Computing, vol. 15, pp. 177–192, 2014. View at Google Scholar
  44. M. Nawaz, E. E. Enscore Jr., and I. Ham, “A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem,” Omega, vol. 11, no. 1, pp. 91–95, 1983. View at Publisher · View at Google Scholar · View at Scopus
  45. R. Ruiz, C. Maroto, and J. Alcaraz, “Two new robust genetic algorithms for the flowshop scheduling problem,” Omega, vol. 34, no. 5, pp. 461–476, 2006. View at Publisher · View at Google Scholar · View at Scopus
  46. J. Carlier and E. Néron, “An exact method for solving the Multi-Processor Flow-Shop,” Operations Research, vol. 34, no. 1, pp. 1–25, 2000. View at Google Scholar · View at Scopus
  47. R. Ruiz and C. Maroto, “A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility,” European Journal of Operational Research, vol. 169, no. 3, pp. 781–800, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. R. Ruiz and T. Stützle, “A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem,” European Journal of Operational Research, vol. 177, no. 3, pp. 2033–2049, 2007. View at Publisher · View at Google Scholar · View at Scopus
  49. C.-J. Liao, C.-T. T. Tseng, and P. Luarn, “A discrete version of particle swarm optimization for flowshop scheduling problems,” Computers and Operations Research, vol. 34, no. 10, pp. 3099–3111, 2007. View at Publisher · View at Google Scholar · View at Scopus
  50. J.-Q. Li, Q.-K. Pan, and Y.-C. Liang, “An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems,” Computers and Industrial Engineering, vol. 59, no. 4, pp. 647–662, 2010. View at Publisher · View at Google Scholar · View at Scopus
  51. J.-Q. Li, Q.-K. Pan, and K.-Z. Gao, “Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems,” International Journal of Advanced Manufacturing Technology, vol. 55, no. 9–12, pp. 1159–1169, 2011. View at Publisher · View at Google Scholar · View at Scopus
  52. J.-Q. Li, Q.-K. Pan, P. N. Suganthan, and T. J. Chua, “A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem,” International Journal of Advanced Manufacturing Technology, vol. 52, no. 5–8, pp. 683–697, 2011. View at Publisher · View at Google Scholar · View at Scopus