About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 359074, 19 pages
http://dx.doi.org/10.1155/2013/359074
Research Article

Using Metaheuristic and Fuzzy System for the Optimization of Material Pull in a Push-Pull Flow Logistics Network

1International Graduate School for Dynamics in Logistics, University of Bremen, c/o BIBA, Hochschulring 20, 28359 Bremen, Germany
2Department of Engineering, Faculty of Engineering and Science, University of Agder, 4879 Grimstad, Norway

Received 31 October 2012; Revised 5 December 2012; Accepted 6 December 2012

Academic Editor: M. Chadli

Copyright © 2013 Afshin Mehrsai 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. Schönsleben, “With agility and adequate partnership strategies towards effective logistics networks,” Computers in Industry, vol. 42, no. 1, pp. 33–42, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Holstrom, K. Framling, J. Tuomi, M. Karkkainen, and T. Ala-Risku, “Implementing collaboration process networks,” International Journal of Logistics Management, vol. 13, no. 2, pp. 39–50, 2002.
  3. R. Suri, Quick Response Manufacturing: A Companywide Approach to Reducing Lead Times, Productivity Pr, 1998.
  4. F. You and I. E. Grossmann, “Design of responsive supply chains under demand uncertainty,” Computers and Chemical Engineering, vol. 32, no. 12, pp. 3090–3111, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Yalcin and R. K. Namballa, “An object-oriented simulation framework for real-time control of automated flexible manufacturing systems,” Computers and Industrial Engineering, vol. 48, no. 1, pp. 111–127, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Zolfaghari and E. Roa, “Cellular manufacturing versus a hybrid system: a comparative study,” Journal of Manufacturing Technology Management, vol. 17, no. 7, pp. 942–996, 2006.
  7. B. Scholz-Reiter and M. Freitag, “Autonomous processes in assembly systems,” CIRP Annals-Manufacturing Technology, vol. 56, no. 2, pp. 712–729, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Ozawa, Institutions, Industrial Upgrading, and Economic Performance in Japan: The “Flying-Geese” Paradigm of Catch-Up Growth, Edward Elgar Pub, Northampton, UK, 2006.
  9. W. Rocky and V. Sridharan, “Linking manufacturing planning and control to the manufacturing environment,” Integrated Manufacturing System, vol. 6, no. 4, pp. 36–42, 1995.
  10. B. Scholz-Reiter, A. Mehrsai, and M. Görges, “Handling dynamics in logistics—adoption of dynamic behaviour and reduction of dynamic effects,” Asian International Journal of Science and Technology Production and Manufacturing Engineering, vol. 2, no. 3, pp. 99–110, 2009.
  11. B. Scholz-Reiter and A. Mehrsai, “Integration of lean-agile experiments with autonomy in supply chains,” in Proceedings 7th International Conference on Manufacturing Research (ICMR09), pp. 60–66, University of Warwick, Warwick, UK, 2009.
  12. M. Spearman, D. Woodruff, and W. Hopp, “CONWIP: a pull alternative to kanban,” International Journal of Production Research, vol. 28, no. 5, pp. 879–894, 1990.
  13. N. Fernandes and S. do Carmo-Silva, “Generic POLCA-A production and materials flow control mechanism for quick response manufacturing,” International Journal of Production Economics, vol. 104, no. 1, pp. 74–84, 2006.
  14. X. Y. Sun, P. Ji, L. Y. Sun, and Y. L. Wang, “Positioning multiple decoupling points in a supply network,” International Journal of Production Economics, vol. 113, no. 2, pp. 943–956, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Gunasekaran, C. Patel, and R. E. McGaughey, “A framework for supply chain performance measurement,” International Journal of Production Economics, vol. 87, no. 3, pp. 333–347, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Petroni and A. Rizzi, “A fuzzy logic based methodology to rank shop floor dispatching rules,” International Journal of Production Economics, vol. 76, no. 1, pp. 99–108, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. B. H. Wang and S. W. He, “Robust optimization model and algorithm for logistics center location and allocation under uncertain environment,” Journal of Transportation Systems Engineering and Information Technology, vol. 9, no. 2, pp. 69–74, 2009. View at Scopus
  18. C. Karrer, “Engineering production control strategies: a guide to tailor strategies that unite the merits of push and pull,” in Engineering Production Control Strategies, pp. 7–36, Springer, München, Germany, 2012.
  19. K. G. Kempf, “An overview of decision policies for production networks,” in Decision Policies for Production Networks, D. Armbruster and K. G. Kempf, Eds., pp. 1–8, Springer, 2012.
  20. J. M. Tien, “Manufacturing and services: from mass production to mass customization,” Journal of Systems Science and Systems Engineering, vol. 20, no. 2, pp. 129–154, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Pahl, S. Voß, and D. L. Woodruff, “Production planning with load dependent lead times,” 4OR Quarterly Journal of Operations Research, vol. 3, no. 4, pp. 257–302, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Germs and J. Riezebos, “Workload balancing capability of pull systems in MTO production,” International Journal of Production Research, vol. 48, no. 8, pp. 2345–2360, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. F. T. S. Chan, “Effect of kanban size on just-in-time manufacturing systems,” Journal of Materials Processing Technology, vol. 116, no. 2-3, pp. 146–160, 2001. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Scholz-Reiter and A. Mehrsai, “Superior performance of leagile supply networks by application of autonomous control,” IFIP Advances in Information and Communication Technology, vol. 338, pp. 333–341, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. R. G. Askin and S. Krishnan, “Defining inventory control points in multiproduct stochastic pull systems,” International Journal of Production Economics, vol. 120, no. 2, pp. 418–429, 2009.
  26. A. Grosfeld-Nir and M. Magazine, “A simulation study of pull systems with ascending/descending buffers and stochastic processing times,” International Journal of Production Research, vol. 43, no. 17, pp. 3529–3541, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. R. Suri and A. Krishnamurthy, “How to plan and implement POLCA—a material control system for high variety or custom-engineered products,” Tech. Rep., Center for Quick Response Manufacturing, Madison University of Wisconsin, USA, 2003.
  28. J. Geraghty and C. Heavey, “A comparison of hybrid push/pull and CONWIP/pull production inventory control policies,” International Journal of Production Economics, vol. 91, no. 1, pp. 75–90, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. R. Mason-Jones, B. Naylor, and D. Towill, “Engineering the leagile supply chain,” International Journal of Agile Management Systems, vol. 2, no. 1, pp. 54–61, 2000.
  30. G. Stone, J. Miemczyk, and R. Esser, “Making build to order a reality: the 5-day car initiative,” Strengthening Competitiveness through Production Networks, vol. 48, no. 16, pp. 4877–4899, 2010.
  31. W. Klingenberg and J. D. Boksma, “A conceptual framework for outsourcing of materials handling activities in automotive: differentiation and implementation,” International Journal of Production Research, vol. 48, no. 16, pp. 4877–4899, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Mehrsai, K. Hamid-Reza, K.-D. Thoben, and B. Scholz-Reiter, “Application of learning pallets for real-time scheduling by the use of radial basis function network,” Neurocomputing, vol. 101, no. 4, pp. 82–93, 2013.
  33. B. Scholz-Reiter, M. Görges, and T. Philipp, “Autonomously controlled production systems-influence of autonomous control level on logistic performance,” CIRP Annals-Manufacturing Technology, vol. 58, no. 1, pp. 395–398, 2009.
  34. A. Mehrsai and B. Scholz-Reiter, “Towards learning pallets applied in pull control job-open shop problem,” in Proceedings of IEEE International Symposium on Assembly and Manufacturing (ISAM '11), pp. 1–6, Tampere, Finland, 2011.
  35. Y. S. Ong and A. J. Keane, “Meta-Lamarckian learning in memetic algorithms,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 2, pp. 99–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  36. F. R. Johnston and J. E. Boylan, “Forecasting for items with intermittent demand,” The Journal of the Operational Research Society, vol. 47, no. 1, pp. 113–121, 1996.
  37. S. Kumar and C. Chandra, “Managing multi-item common vendor inventory system with random demands,” International Journal of Physical Distribution & Logistics Management, vol. 32, no. 3, pp. 188–202, 2002.
  38. N. Kubota and T. Fukuda, “Structured intelligence for self-organizing manufacturing systems,” Journal of Intelligent Manufacturing, vol. 10, no. 2, pp. 121–133, 1999. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Schönberger, “Operational freight carrier planning,” in Advanced Planning and Scheduling in Process Industry, H. O. Günther and P. V. Beek, Eds., Springer, Heidelbelg, Germany, 2005.
  40. T. Weise, “Global Optimization Algorithms—Theory and Application,” Abrufdatum, vol. 24, no. 1, 2008, http://www.it-weise.de/.
  41. E. Cantú-Paz, “Migration policies, selection pressure, and parallel evolutionary algorithms,” Journal of Heuristics, vol. 7, no. 4, pp. 311–334, 2001. View at Publisher · View at Google Scholar · View at Scopus
  42. 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
  43. A. Tarighat and A. Miyamoto, “Fuzzy concrete bridge deck condition rating method for practical bridge management system,” Expert Systems with Applications, vol. 36, no. 10, pp. 112077–112085, 2009.
  44. B. K. Wong and V. S. Lai, “A survey of the application of fuzzy set theory in production and operations management,” International Journal of Production Economics, vol. 129, no. 1, pp. 157–168, 2011.
  45. M. Sakawa and R. Kubota, “Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms,” European Journal of Operational Research, vol. 120, no. 2, pp. 393–407, 2000. View at Publisher · View at Google Scholar · View at Scopus
  46. S. Petrovic, C. Fayad, D. Petrovic, E. Burke, and G. Kendall, “Fuzzy job shop scheduling with lot-sizing,” Annals of Operations Research, vol. 159, no. 1, pp. 275–292, 2008. View at Publisher · View at Google Scholar · View at Scopus
  47. B. Y. Qu and P. N. Suganthan, “Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection,” Information Sciences, vol. 180, no. 17, pp. 3170–3181, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. C. Fayad and S. Petrovic, “A fuzzy genetic algorithm for real-world job-shop scheduling,” in Innovations in Applied Artificial Intelligence, M. Ali and F. Esposito, Eds., vol. 3533 of Lecture Notes in Artificial Intelligence, pp. 524–533, Springer, Berlin, Germany, 2005.
  49. L. Vermeiren, T. M. Guerra, and H. Lamara, “Application of practical fuzzy arithmetic to fuzzy internal model control,” Engineering Applications of Artificial Intelligence, vol. 24, no. 6, pp. 1006–1017, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. Y. Thorani, P. P. B. Rao, and N. R. Shankar, “Ordering generalized trapezoidal fuzzy numbers using orthocentre of centroids,” International Journal of Algebra, vol. 6, no. 22, pp. 1069–1085, 2012.
  51. P. Biswas and S. Pramanik, “Application of fuzzy ranking method to determine the replacement time for fuzzy replacement problem,” International Journal of Computer Applications, vol. 25, no. 11, pp. 41–47, 2011.
  52. W. Jianqiang and Z. Zhong, “Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision making problems,” Journal of Systems Engineering and Electronics, vol. 20, no. 2, pp. 321–326, 2009. View at Scopus
  53. D. Chakraborty and D. Guha, “Addition of two generalized fuzzy numbers,” International Journal of Industrial Mathematics, vol. 2, no. 1, pp. 9–20, 2010.
  54. S. M. Chen and J. H. Chen, “Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads,” Expert Systems with Applications, vol. 36, no. 3, pp. 6833–6842, 2009.
  55. P. Rao, P. Bushan, and N. R. Shankar, “Ranking fuzzy numbers with a distance method using circumcenter of centroids and an index of modality,” Advances in Fuzzy Systems, vol. 2011, Article ID 178308, 7 pages, 2011. View at Publisher · View at Google Scholar
  56. R. T. Marler and J. S. Arora, “Survey of multi-objective optimization methods for engineering,” Structural and Multidisciplinary Optimization, vol. 26, no. 6, pp. 369–395, 2004.
  57. A. Mehrsai, K. Hamid-Reza, and B. Scholz-Reiter, “Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system,” The International Journal of Advanced Manufacturing Technology, vol. 64, no. 5–8, pp. 1131–1150.