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The Scientific World Journal
Volume 2014, Article ID 730314, 13 pages
http://dx.doi.org/10.1155/2014/730314
Research Article

An Approximation Solution to Refinery Crude Oil Scheduling Problem with Demand Uncertainty Using Joint Constrained Programming

1Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China
2College of Mathematics and Information, Shanghai Lixin University of Commerce, China

Received 1 December 2013; Accepted 20 January 2014; Published 16 March 2014

Academic Editors: Z. Chen, W.-C. Hong, and K.-C. Ying

Copyright © 2014 Qianqian Duan 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. J. M. Pinto, M. Joly, and L. F. L. Moro, “Planning and scheduling models for refinery operations,” Computers and Chemical Engineering, vol. 24, no. 9-10, pp. 2259–2276, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Li, I. A. Karimi, and R. Srinivasan, “Recipe determination and scheduling of gasoline blending operations,” AIChE Journal, vol. 56, no. 2, pp. 441–465, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Li, R. Misener, and C. A. Floudas, “Continuous-time modeling and global optimization approach for scheduling of crude oil operations,” AIChE Journal, vol. 58, no. 1, pp. 205–226, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Jia, M. Ierapetritou, and J. D. Kelly, “Refinery short-term scheduling using continuous time formulation: Crude-oil operations,” Industrial and Engineering Chemistry Research, vol. 42, no. 13, pp. 3085–3097, 2003. View at Google Scholar · View at Scopus
  5. H. Lee, J. M. Pinto, I. E. Grossmann, and S. Park, “Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management,” Industrial and Engineering Chemistry Research, vol. 35, no. 5, pp. 1630–1641, 1996. View at Google Scholar · View at Scopus
  6. L. Wenkai, C.-W. Hui, B. Hua, and Z. Tong, “Scheduling crude oil unloading, storage, and processing,” Industrial and Engineering Chemistry Research, vol. 41, no. 26, pp. 6723–6734, 2002. View at Google Scholar · View at Scopus
  7. P. C. P. Reddy, I. A. Karimi, and R. Srinivasan, “Novel solution approach for optimizing crude oil operations,” AIChE Journal, vol. 50, no. 6, pp. 1177–1197, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Chryssolouris, N. Papakostas, and D. Mourtzis, “Refinery short-term scheduling with tank farm, inventory and distillation management: an integrated simulation-based approach,” European Journal of Operational Research, vol. 166, no. 3, pp. 812–827, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Hu and Y. Zhu, “An asynchronous time slot-based continuous time for mulation approach for crude oil scheduling,” Computers and Applied Chemistry, vol. 24, pp. 713–719, 2007. View at Google Scholar
  10. Z. Jia and M. Ierapetritou, “Efficient short-term scheduling of refinery operations based on a continuous time formulation,” Computers and Chemical Engineering, vol. 28, no. 6-7, pp. 1001–1019, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Jia, M. Ierapetritou, and J. D. Kelly, “Refinery short-term scheduling using continuous time formulation: crude-oil operations,” Industrial and Engineering Chemistry Research, vol. 42, no. 13, pp. 3085–3097, 2003. View at Google Scholar · View at Scopus
  12. S. M. Neiro and J. M. Pinto, “Supply chain optimization of petroleum refinery complexes,” in Proceedings of the 4th International Conference on Foundations of Computer-Aided Process Operations, pp. 59–72, 2003.
  13. J. Wang and G. Rong, “Robust optimization model for crude oil scheduling under uncertainty,” Industrial and Engineering Chemistry Research, vol. 49, no. 4, pp. 1737–1748, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Cao and X. Gu, “Chance constrained programming with fuzzy parameters for re_nery crude oil scheduling problem,” in Fuzzy Systems and Knowledge Discovery, pp. 1010–1019, 2006. View at Google Scholar
  15. C. Cao, X. Gu, and Z. Xin, “Chance constrained programming models for refinery short-term crude oil scheduling problem,” Applied Mathematical Modelling, vol. 33, no. 3, pp. 1696–1707, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Cao, X. Gu, and Z. Xin, “Stochastic chance constrained mixed-integer nonlinear programming models and the solution approaches for refinery short-term crude oil scheduling problem,” Applied Mathematical Modelling, vol. 34, no. 11, pp. 3231–3243, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. S. B. Petkov and C. D. Maranas, “Multiperiod Planning and Scheduling of Multiproduct Batch Plants under Demand Uncertainty,” Industrial and Engineering Chemistry Research, vol. 36, no. 11, pp. 4864–4881, 1997. View at Google Scholar · View at Scopus
  18. Y. L. Tong, Probability Inequalities for Multivariate Distributions, Academic Press, New York, NY, USA, 1980.
  19. B. Fleischmann and H. Meyr, “The general lotsizing and scheduling problem,” OR Spectrum, vol. 19, no. 1, pp. 11–21, 1997. View at Google Scholar · View at Scopus
  20. A. Charnes and W. W. Cooper, “Normal deviates and chance constraints,” Journal of the American Statistical Association, vol. 52, p. 134, 1962. View at Google Scholar
  21. U. A. Ozturk, M. Mazumdar, and B. A. Norman, “A solution to the stochastic unit commitment problem using chance constrained programming,” IEEE Transactions on Power Systems, vol. 19, no. 3, pp. 1589–1598, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Billingsley, Probability and Measure, John Wiley & Sons, New York, NY, USA, 2nd edition, 1986.
  23. F. N. Lee, M.-Y. Lin, and A. M. Breipohl, “Evaluation of the variance of production cost using a stochastic outage capacity state model,” IEEE Transactions on Power Systems, vol. 5, no. 4, pp. 1061–1067, 1990. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Genz and F. Bretz, “Comparison of methods for the computation of multivariate t probabilities,” Journal of Computational and Graphical Statistics, vol. 11, no. 4, pp. 950–971, 2002. View at Publisher · View at Google Scholar · View at Scopus