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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 657465, 12 pages
http://dx.doi.org/10.1155/2013/657465
Research Article

Modeling and Optimization of Stochastic Joint Replenishment and Delivery Scheduling Problem with Uncertain Costs

1School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
2School of Information Management, Central China University, Wuhan 430079, China

Received 26 March 2013; Accepted 16 July 2013

Academic Editor: Mustapha Ait Rami

Copyright © 2013 Lin Wang 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. F. T. Shu, “Economic ordering frequency for two items jointly replenished,” Management Science, vol. 17, no. 6, pp. B406–B410, 1971. View at Scopus
  2. S. K. Goyal, “Determination of economic packaging frequency for items jointly replenished,” Management Science, vol. 20, no. 2, pp. 232–235, 1973. View at Scopus
  3. M. Khouja and S. Goyal, “A review of the joint replenishment problem literature: 1989–2005,” European Journal of Operational Research, vol. 186, no. 1, pp. 1–16, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  4. P. Robinson, A. Narayanan, and F. Sahin, “Coordinated deterministic dynamic demand lot-sizing problem: a review of models and algorithms,” Omega, vol. 37, no. 1, pp. 3–15, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. W. W. Qu, J. H. Bookbinder, and P. Iyogun, “Integrated inventory-transportation system with modified periodic policy for multiple products,” European Journal of Operational Research, vol. 115, no. 2, pp. 254–269, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Wang, C. X. Dun, W. J. Bi, and Y. R. Zeng, “An effective and efficient differential evolution algorithm for the integrated stochastic joint replenishment and delivery model,” Knowledge-Based Systems, vol. 36, pp. 104–114, 2012.
  7. S. Sindhuchao, H. E. Romeijn, E. Akçali, and R. Boondiskulchok, “An integrated inventory-routing system for multi-item joint replenishment with limited vehicle capacity,” Journal of Global Optimization, vol. 32, no. 1, pp. 93–118, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  8. C. K. Chan, L. Y.-O. Li, C. T. Ng, B. K.-S. Cheung, and A. Langevin, “Scheduling of multi-buyer joint replenishments,” International Journal of Production Economics, vol. 102, no. 1, pp. 132–142, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. B. C. Cha, I. K. Moon, and J. H. Park, “The joint replenishment and delivery scheduling of the one-warehouse, n-retailer system,” Transportation Research Part E, vol. 44, no. 5, pp. 720–730, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. I. K. Moon, B. C. Cha, and C. U. Lee, “The joint replenishment and freight consolidation of a warehouse in a supply chain,” International Journal of Production Economics, vol. 133, no. 1, pp. 344–350, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. R. Zeng, L. Wang, and J. He, “A novel approach for evaluating control criticality of spare parts using fuzzy comprehensive evaluation and GRA,” International Journal of Fuzzy Systems, vol. 14, no. 3, pp. 392–401, 2012.
  12. S. H. Chen and S. M. Chang, “Optimization of fuzzy production inventory model with unrepairable defective products,” International Journal of Production Economics, vol. 113, no. 2, pp. 887–894, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Wang, Q.-L. Fu, and Y.-R. Zeng, “Continuous review inventory models with a mixture of backorders and lost sales under fuzzy demand and different decision situations,” Expert Systems with Applications, vol. 39, no. 4, pp. 4181–4189, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. O. Dey and D. Chakraborty, “A single period inventory model with a truncated normally distributed fuzzy random variable demand,” International Journal of Systems Science, vol. 43, no. 3, pp. 518–525, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  15. Y.-J. Lin, “A periodic review inventory model involving fuzzy expected demand short and fuzzy backorder rate,” Computers and Industrial Engineering, vol. 54, no. 3, pp. 666–676, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Wang, C. X. Dun, C. G. Lee, Q. L. Fu, and Y. R. Zeng, “Model and algorithm for fuzzy joint replenishment and delivery scheduling without explicit membership function,” International Journal of Advanced Manufacturing Technology, vol. 66, no. 9–12, pp. 1907–1920, 2013.
  17. E. Arkin, D. Joneja, and R. Roundy, “Computational complexity of uncapacitated multi-echelon production planning problems,” Operations Research Letters, vol. 8, no. 2, pp. 61–66, 1989. View at MathSciNet · View at Scopus
  18. S. K. Goyal, “Determination of optimum packaging frequency of items jointly replenished,” Management Science, vol. 21, no. 4, pp. 436–443, 1974. View at Scopus
  19. F.-C. Lee and M.-J. Yao, “A global optimum search algorithm for the joint replenishment problem under power-of-two policy,” Computers and Operations Research, vol. 30, no. 9, pp. 1319–1333, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Kaspi and M. J. Rosenblatt, “On the economic ordering quantity for jointly replenished items,” International Journal of Production Research, vol. 29, no. 1, pp. 107–114, 1991. View at Scopus
  21. A. L. Olsen, “An evolutionary algorithm to solve the joint replenishment problem using direct grouping,” Computers and Industrial Engineering, vol. 48, no. 2, pp. 223–235, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. E. P. Robinson and L.-L. Gao, “A dual ascent procedure for multiproduct dynamic demand coordinated replenishment with backlogging,” Management Science, vol. 42, no. 11, pp. 1556–1564, 1996. View at Scopus
  23. E. P. Robinson, A. Narayanan, and L.-L. Gao, “Effective heuristics for the dynamic demand joint replenishment problem,” Journal of the Operational Research Society, vol. 58, no. 6, pp. 808–815, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. D. E. Blumenfeld, L. D. Burns, C. F. Daganzo, M. C. Frick, and R. W. Hall, “Reducing logistics costs at general motors,” Interfaces, vol. 17, no. 1, pp. 26–47, 1987. View at Scopus
  25. A. Eynan and D. H. Kropp, “Periodic review and joint replenishment in stochastic demand environments,” IIE Transactions, vol. 30, no. 11, pp. 1025–1033, 1998. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Vujošević, D. Petrović, and R. Petrović, “EOQ gormula when inventory is fuzzy,” International Journal of Production Economics, vol. 45, no. 1–3, pp. 499–504, 1996.
  27. S. Pramanik and P. Biswas, “Multi-objective assignment problem with generalized trapezoidal fuzzy numbers,” International Journal of Applied Information Systems, vol. 2, no. 6, pp. 13–20, 2012.
  28. R. E. Giachetti and R. E. Young, “Analysis of the error in the standard approximation used for multiplication of triangular and trapezoidal fuzzy numbers and the development of a new approximation,” Fuzzy Sets and Systems, vol. 91, no. 1, pp. 1–13, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  29. S. H. Chen and C. H. Hsieh, “Graded mean integration representation of generalized fuzzy number,” Journal of Chinese Fuzzy Systems, vol. 5, no. 2, pp. 1–7, 1997.
  30. G. C. Mahata and P. Mahata, “Analysis of a fuzzy economic order quantity model for deteriorating items under retailer partial trade credit financing in a supply chain,” Mathematical and Computer Modelling, vol. 53, no. 9-10, pp. 1621–1636, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  31. Y.-M. Wang, J.-B. Yang, D.-L. Xu, and K.-S. Chin, “On the centroids of fuzzy numbers,” Fuzzy Sets and Systems, vol. 157, no. 7, pp. 919–926, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  32. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  33. L. Wang, J. He, and Y.-R. Zeng, “A differential evolution algorithm for joint replenishment problem using direct grouping and its application,” Expert Systems, vol. 29, no. 5, pp. 429–441, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. H.-M. Wee, C.-C. Lo, and P.-H. Hsu, “A multi-objective joint replenishment inventory model of deteriorated items in a fuzzy environment,” European Journal of Operational Research, vol. 197, no. 2, pp. 620–631, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  35. T. Zheng and M. Yamashiro, “Solving flow shop scheduling problems by quantum differential evolutionary algorithm,” International Journal of Advanced Manufacturing Technology, vol. 49, no. 5–8, pp. 643–662, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Salman, A. P. Engelbrecht, and M. G. H. Omran, “Empirical analysis of self-adaptive differential evolution,” European Journal of Operational Research, vol. 183, no. 2, pp. 785–804, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. F. Neri and V. Tirronen, “Recent advances in differential evolution: a survey and experimental analysis,” Artificial Intelligence Review, vol. 33, no. 1-2, pp. 61–106, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. J. Liu and J. Lampinen, “On setting the control parameter of the differential evolution algorithm,” in Proceedings of the 8th International Mendel Conference on Soft Computing, pp. 11–18, 2002.