Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 179085, 19 pages
http://dx.doi.org/10.1155/2014/179085
Research Article

Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model

1Department of Mechanical Engineering, Centre for Product Design and Manufacturing, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
2Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang Darul Makmur, Malaysia

Received 8 November 2013; Revised 20 February 2014; Accepted 11 March 2014; Published 8 May 2014

Academic Editor: Olabisi Falowo

Copyright © 2014 Farzad Tahriri 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. Z. X. Guo, W. K. Wong, S. Y. S. Leung, J. T. Fan, and S. F. Chan, “Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: a case study based on the apparel industry,” Computers & Industrial Engineering, vol. 50, no. 3, pp. 202–219, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. S. O. Tasan and S. Tunali, “A review of the current applications of genetic algorithms in assembly line balancing,” Journal of Intelligent Manufacturing, vol. 19, no. 1, pp. 49–69, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Kriengkorakot and N. Pianthong, “The assembly line balancing problem: review articles,” KKU Engineering Journal, vol. 34, no. 2, pp. 133–140, 2012. View at Google Scholar
  4. I. Baybars, “A survey of exact algorithms for the simple assembly line balancing problem,” Management Science, vol. 32, no. 8, pp. 909–932, 1986. View at Google Scholar · View at Scopus
  5. Z. Taha, F. Tahriri, and A. Zuhdi, “Job sequencing and layout optimization in virtual production line,” Journal of Quality, vol. 18, no. 4, pp. 351–374, 2011. View at Google Scholar · View at Scopus
  6. P. Brucker and T. Kampmeyer, “Cyclic job shop scheduling problems with blocking,” Annals of Operations Research, vol. 159, no. 1, pp. 161–181, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Heike, M. Ramulu, E. Sorenson, P. Shanahan, and K. Moinzadeh, “Mixed model assembly alternatives for low-volume manufacturing: the case of the aerospace industry,” International Journal of Production Economics, vol. 72, no. 2, pp. 103–120, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. A. R. Rahimi-Vahed, M. Rabbani, R. Tavakkoli-Moghaddam, S. A. Torabi, and F. Jolai, “A multi-objective scatter search for a mixed-model assembly line sequencing problem,” Advanced Engineering Informatics, vol. 21, no. 1, pp. 85–99, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Javadi, A. Rahimi-Vahed, M. Rabbani, and M. Dangchi, “Solving a multi-objective mixed-model assembly line sequencing problem by a fuzzy goal programming approach,” International Journal of Advanced Manufacturing Technology, vol. 39, no. 9-10, pp. 975–982, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Boysen, M. Fliedner, and A. Scholl, “Sequencing mixed-model assembly lines: survey, classification and model critique,” European Journal of Operational Research, vol. 192, no. 2, pp. 349–373, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Monden, Toyota Production System: Practical Approach to Production Management, Industrial Engineering and Management Press, Institute of Industrial Engineers, Norcross, Ga, USA, 1997.
  12. J. F. Bard, E. Dar-El, and A. Shtub, “Analytic framework for sequencing mixed model assembly lines,” The International Journal of Production Research, vol. 30, no. 1, pp. 35–48, 1992. View at Google Scholar · View at Scopus
  13. E. M. Dar-El, “Mixed-model assembly line sequencing problems,” Omega, vol. 6, no. 4, pp. 313–323, 1978. View at Google Scholar · View at Scopus
  14. E. M. Dar-El and R. F. Cother, “Assembly line sequencing for model mix,” The International Journal of Production Research, vol. 13, no. 5, pp. 463–477, 1975. View at Google Scholar · View at Scopus
  15. E. M. Dar-El and A. Nadivi, “A mixed-model sequencing application,” The International Journal of Production Research, vol. 19, no. 1, pp. 69–84, 1981. View at Google Scholar
  16. C. Merengo, F. Nava, and A. Pozzetti, “Balancing and sequencing manual mixed-model assembly lines,” The International Journal of Production Research, vol. 37, no. 12, pp. 2835–2860, 1999. View at Google Scholar · View at Scopus
  17. X. Zhu, S. J. Hu, Y. Koren, S. P. Marin, and N. Huang, “Sequence planning to minimize complexity in mixed-model assembly lines,” in Proceedings of the IEEE International Symposium on Assembly and Manufacturing (ISAM '07), pp. 251–258, July 2007. View at Scopus
  18. B. Rekiek, P. De Lit, and A. Delchambre, “Designing mixed-product assembly lines,” IEEE Transactions on Robotics and Automation, vol. 16, no. 3, pp. 268–280, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. P. De Lit, A. Delchambre, and J.-M. Henrioud, “An integrated approach for product family and assembly system design,” IEEE Transactions on Robotics and Automation, vol. 19, no. 2, pp. 324–334, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Bukchin, E. M. Dar-El, and J. Rubinovitz, “Mixed model assembly line design in a make-to-order environment,” Computers & Industrial Engineering, vol. 41, no. 4, pp. 405–421, 2001. View at Google Scholar · View at Scopus
  21. C. Moon, M. Lee, Y. Seo, and Y. H. Lee, “Integrated machine tool selection and operation sequencing with capacity and precedence constraints using genetic algorithm,” Computers & Industrial Engineering, vol. 43, no. 3, pp. 605–621, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Khan and A. J. Day, “A knowledge based design methodology for manufacturing assembly lines,” Computers & Industrial Engineering, vol. 41, no. 4, pp. 441–467, 2001. View at Google Scholar · View at Scopus
  23. A. R. Mendes, A. L. Ramos, A. S. Simaria, and P. M. Vilarinho, “Combining heuristic procedures and simulation models for balancing a PC camera assembly line,” Computers & Industrial Engineering, vol. 49, no. 3, pp. 413–431, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Joly and Y. Frein, “Heuristics for an industrial car sequencing problem considering paint and assembly shop objectives,” Computers & Industrial Engineering, vol. 55, no. 2, pp. 295–310, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Ghosh and R. J. Gagnon, “Comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems,” The International Journal of Production Research, vol. 27, no. 4, pp. 637–670, 1989. View at Google Scholar · View at Scopus
  26. A. Norozi, M. K. Ariffin, and N. Ismail, “Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line,” American Journal of Engineering and Applied Sciences, vol. 3, no. 1, pp. 831–840, 2010. View at Google Scholar
  27. W. K. Wong, C. K. Chan, and W. H. Ip, “Optimization of spreading and cutting sequencing model in garment manufacturing,” Computers in Industry, vol. 43, no. 1, pp. 1–10, 2000. View at Publisher · View at Google Scholar · View at Scopus
  28. S. G. Ponnambalam, P. Aravindan, and M. Subba Rao, “Genetic algorithms for sequencing problems in mixed model assembly lines,” Computers & Industrial Engineering, vol. 45, no. 4, pp. 669–690, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. S.-M. Im and J.-J. Lee, “Adaptive crossover, mutation and selection using fuzzy system for genetic algorithms,” Artificial Life and Robotics, vol. 13, no. 1, pp. 129–133, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Subbu, A. C. Sanderson, and P. P. Bonissone, “Fuzzy logic controlled genetic algorithms versus tuned genetic algorithms: an agile manufacturing application,” in Proceedings of the IEEE International Symposium on Intelligent Control (ISIC '98), pp. 434–440, September 1998. View at Scopus
  31. M. Gen, R. Chen, and L. Lin, Network Models and Optimization: Multiobjective Genetic Algorithm Approach, Springer, New York, NY, USA, 2008.
  32. B. S. P. Reddy and C. S. P. Rao, “A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS,” International Journal of Advanced Manufacturing Technology, vol. 31, no. 5-6, pp. 602–613, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. P. T. Zacharia and A. C. Nearchou, “Multi-objective fuzzy assembly line balancing using genetic algorithms,” Journal of Intelligent Manufacturing, vol. 23, no. 3, pp. 615–627, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. P. R. McMullen, “An efficient frontier approach to addressing JIT sequencing problems with setups via search heuristics,” Computers & Industrial Engineering, vol. 41, no. 3, pp. 335–353, 2001. View at Publisher · View at Google Scholar · View at Scopus
  35. P. R. McMullen, “An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives,” Artificial Intelligence in Engineering, vol. 15, no. 3, pp. 309–317, 2001. View at Publisher · View at Google Scholar · View at Scopus
  36. P. R. McMullen, “A Kohonen self-organizing map approach to addressing a multiple objective, mixed-model JIT sequencing problem,” International Journal of Production Economics, vol. 72, no. 1, pp. 59–71, 2001. View at Publisher · View at Google Scholar · View at Scopus
  37. P. R. McMullen and P. Tarasewich, “A beam search heuristic method for mixed-model scheduling with setups,” International Journal of Production Economics, vol. 96, no. 2, pp. 273–283, 2005. View at Publisher · View at Google Scholar · View at Scopus
  38. J. F. Bard, A. Shtub, and S. B. Joshi, “Sequencing mixed-model assembly lines to level parts usage and minimize line length,” The International Journal of Production Research, vol. 32, no. 10, pp. 2431–2454, 1994. View at Google Scholar
  39. P. R. McMullen, “JIT sequencing for mixed-model assembly lines with setups using Tabu Search,” Production Planning & Control, vol. 9, no. 5, pp. 504–510, 1998. View at Google Scholar · View at Scopus
  40. P. R. McMullen and G. V. Frazier, “A simulated annealing approach to mixed-model sequencing with multiple objectives on a just-in-time line,” IIE Transactions, vol. 32, no. 8, pp. 679–686, 2000. View at Google Scholar · View at Scopus
  41. V. Giard and J. Jeunet, “Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line,” International Journal of Production Economics, vol. 123, no. 2, pp. 290–300, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. D. J. Fonseca, C. L. Guest, M. Elam, and C. L. Karr, “A fuzzy logic approach to assembly line balancing,” Mathware & Soft Computing, vol. 12, no. 1, pp. 57–74, 2005. View at Google Scholar
  43. M. Gen, Y. Tsujimura, and Y. Li, “Fuzzy assembly line balancing using genetic algorithms,” Computers & Industrial Engineering, vol. 31, no. 3-4, pp. 631–634, 1996. View at Google Scholar · View at Scopus
  44. Y. Tsujimura, M. Gen, and E. Kubota, “Solving fuzzy assembly-line balancing problem with genetic algorithms,” Computers & Industrial Engineering, vol. 29, no. 1-4, pp. 543–547, 1995. View at Google Scholar · View at Scopus
  45. W. Cheung and H. Zhou, “Using genetic algorithms and heuristics for job shop scheduling with sequence-dependent setup times,” Annals of Operations Research, vol. 107, no. 1–4, pp. 65–81, 2001. View at Publisher · View at Google Scholar · View at Scopus
  46. G. Mosheiov, A. Sarig, and J. Sidney, “The Browne-Yechiali single-machine sequence is optimal for flow-shops,” Computers and Operations Research, vol. 37, no. 11, pp. 1965–1967, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Silberholz and B. Golden, “Comparison of metaheuristics,” in Handbook of Metaheuristics, Springer, New York, NY, USA, 2010. View at Google Scholar