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International Journal of Mathematics and Mathematical Sciences
Volume 2014, Article ID 690435, 14 pages
http://dx.doi.org/10.1155/2014/690435
Research Article

Optimal Manufacturing-Remanufacturing Production Policy for a Closed-Loop Supply Chain under Fill Rate and Budget Constraint in Bifuzzy Environments

1Department of Applied Mathematics, University of Calcutta, 92 APC Road, Kolkata, West Bengal 700009, India
2Department of Applied Science, Haldia Institute of Technology, Purba Midnapur, West Bengal 721657, India

Received 31 December 2013; Accepted 12 May 2014; Published 24 June 2014

Academic Editor: Tamer Eren

Copyright © 2014 Soumita Kundu 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. E. van der Laan, M. Salomon, R. Dekker, and L. van Wassenhove, “Inventory control in hybrid systems with remanufacturing,” Management Science, vol. 45, no. 5, pp. 733–747, 1999. View at Google Scholar · View at Scopus
  2. H. Krikke, “Impact of closed-loop network configurations on carbon footprints: a case study in copiers,” Resources, Conservation and Recycling, vol. 55, no. 12, pp. 1196–1205, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Inderfurth, “Optimal policies in hybrid manufacturing/remanufacturing systems with product substitution,” International Journal of Production Economics, vol. 90, no. 3, pp. 325–343, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. I. Dobos and K. Richter, “An extended production/recycling model with stationary demand andreturn rates,” International Journal of Production Economics, vol. 90, pp. 311–323, 2004. View at Google Scholar
  5. D.-W. Choi, H. Hwang, and S.-G. Koh, “A generalized ordering and recovery policy for reusable items,” European Journal of Operational Research, vol. 182, no. 2, pp. 764–774, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Rubio and A. Corominas, “Optimal manufacturing-remanufacturing policies in a lean production environment,” Computers and Industrial Engineering, vol. 55, no. 1, pp. 234–242, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. C. A. Yano and L. H. Lee, “Lot sizing with random yields: a review,” Operations Research, vol. 43, no. 2, pp. 311–334, 1995. View at Google Scholar
  8. A. Hsu and Y. Bassok, “Random yield and random demand in a production system with downward substitution,” Operations Research, vol. 47, no. 2, pp. 277–290, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  9. S. Bollapragada and T. E. Morton, “Myopic heuristics for the random yield problem,” Operations Research, vol. 47, no. 5, pp. 713–722, 1999. View at Google Scholar · View at Scopus
  10. B. Kazaz, “Production planning under yield and demand uncertainty with yield-dependent cost and price,” Manufacturing and Service Operations Management, vol. 6, no. 3, pp. 209–224, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Steven, “Inventory control subject to uncertain demand,” in Production and Operations Analysis, pp. 255–261, McGraw-Hill Irwin, New York, NY, USA, 5th edition, 2005. View at Google Scholar
  12. P. H. Zipkin, Foundations of Inventory Management, McGraw-Hill, New York, NY, USA, 2000.
  13. S. Axsäter, “A simple procedure for determining order quantities under a fill rate constraint and normally distributed lead-time demand,” European Journal of Operational Research, vol. 174, no. 1, pp. 480–491, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, no. 3, pp. 338–353, 1965. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. D. K. Jana, B. Das, and T. K. Roy, “A partial backlogging inventory model for deteriorating item under fuzzy inflation and discounting over random planning horizon: a fuzzy genetic algorithm approach,” Advances in Operations Research, vol. 2013, Article ID 973125, 13 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  16. D. K. Jana, K. Maity, B. Das, and T. K. Roy, “A fuzzy simulation via contractive mapping genetic algorithm approach to an imprecise production inventory model under volume flexibility,” Journal of Simulation, vol. 7, no. 2, pp. 90–100, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. D. K. Jana, B. Das, and M. Maiti, “Multi-item partial backlogging inventory models over random planning horizon in Random Fuzzy environment,” Applied Soft Computing, vol. 21, pp. 12–27, 2014. View at Google Scholar
  18. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning. I,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  19. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning-II,” Information Sciences, vol. 8, no. 4, pp. 301–357, 1975. View at Publisher · View at Google Scholar
  20. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning-III,” Information Sciences, vol. 9, no. 1, pp. 43–80, 1975. View at Publisher · View at Google Scholar
  21. J. M. Mendel, John, and R.I. B, “Type-2 fuzzy sets made simple,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 117–127, 2002. View at Google Scholar
  22. L. A. Zadeh, “Quantitative fuzzy semantics,” Information Sciences, vol. 3, no. 2, pp. 159–176, 1971. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  23. S. Gottwald, “Set theory for fuzzy sets of higher level,” Fuzzy Sets and Systems, vol. 2, no. 2, pp. 125–151, 1979. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  24. J. Xu and X. Zhou, Fuzzy Link Multiple-Objective Decision Making, Springer, Berlin, Germany, 2009.
  25. S. Pramanik, D. K. Jana, and M. Maiti, “Multi-objective solid transportation problem in imprecise environments,” Journal of Transportation Security, vol. 6, no. 2, pp. 131–150, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Liu, Theory and Practice of Uncertain Programming, Physica, Heidelberg, Germany, 2002.