Table of Contents Author Guidelines Submit a Manuscript
Journal of Optimization
Volume 2013, Article ID 960879, 12 pages
http://dx.doi.org/10.1155/2013/960879
Research Article

Dynamic Optimization Technique for Distribution of Goods with Stochastic Shortages

Department of Mathematics, Faculty of Physical Sciences, University of Benin, P.M.B. 1154, Benin City, Edo State, Nigeria

Received 2 April 2013; Accepted 24 September 2013

Academic Editor: Eric S. Fraga

Copyright © 2013 Charles I. Nkeki. 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. C. R. Nwozo and C. I. Nkeki, “On distribution of goods using dynamic programming principles with error bounds,” Journal of Applied Mathematics and Bioinformatics, vol. 2, no. 1, pp. 1–19, 2012. View at Google Scholar
  2. W. B. Powell, “A comparative review of alternative algorithms for the dynamic vehicle allocation problem,” in Vehicle Routing: Methods and Studies, B. Golden and A. Assad, Eds., pp. 249–292, North-Holland, Amsterdam, The Netherlands, 1988. View at Google Scholar
  3. R. K. Cheung and W. B. Powell, “AN algorithm for multistage dynamic networks with random arc capacities, with an application to dynamic fleet management,” Operations Research, vol. 44, no. 6, pp. 951–963, 1996. View at Google Scholar · View at Scopus
  4. G. A. Godfrey and W. B. Powell, “An adaptive dynamic programming algorithm for dynamic fleet management, I: single period travel times,” Transportation Science, vol. 36, no. 1, pp. 21–39, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. B. van Roy, D. P. Bertsekas, Y. Lee, and J. N. Tsitsiklis, “A neuro-dynamic programming approach to retailer inventory management,” in Proceedings of the 36th IEEE Conference on Decision and Control, pp. 4052–4057, December 1997. View at Scopus
  6. J. M. Mulvey and H. Vladimirou, “Stochastic network programming for financial planning problems,” Management Science, vol. 38, no. 11, pp. 1642–1664, 1992. View at Publisher · View at Google Scholar
  7. J. M. Mulvey and A. J. Ruszczynski, “A new scenario decomposition method for large-scale stochastic optimization,” Operations Research, vol. 43, no. 3, pp. 477–490, 1995. View at Publisher · View at Google Scholar
  8. W. B. Powell, A. Ruszczynski, and H. Topaloglu, “Learning algorithms for separable approximations of stochastic optimization problems,” Tech. Rep., Department of Operations Research and Financial Engineering, Princeton University, 2002. View at Google Scholar
  9. W. B. Powell and H. Topaloglu, “Stochastic programming in transportation and logistics,” in Handbook in Operation Research and Management Science, A. Ruszczynski and A. Shapiro, Eds., pp. 249–292, Elsevier, Amsterdam, The Netherlands, 2003, Volume on Stochastic Programming. View at Google Scholar
  10. B. W. Powell and B. van Roy, “Approximate dynamic programming for high-dimensional resource allocation problems,” in Handbook of Learning and Approximate Dynamic Programming, IEEE Press, New York, NY, USA, 2003. View at Google Scholar
  11. C. Guestrin, D. Koller, and R. Parr, “Efficient solution algorithms for factored MDPs,” Tech. Rep., 2003. View at Google Scholar
  12. M. Z. Spivey and W. B. Powell, “The dynamic assignment problem,” Transportation Science, vol. 38, no. 4, pp. 399–419, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Cogill, M. Rotkowitz, B. van Roy, and S. Lall, An Approximate Dynamic Programming Approach to Decentralized Control of Stochastic Systems, Stanford University Press, 2004.
  14. H. Topaloglu and S. Kunnumkal, “Approximate dynamic programming methods for an inventory allocation problem under uncertainty,” Naval Research Logistics, vol. 53, no. 8, pp. 822–841, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Hauskrecht and T. Singliar, “Monte-Carlo optimizations for resource allocation problems in stochastic network systems,” http://arxiv.org/abs/1212.2481.
  16. K. Ozbay, W. Xiao, C. Iyigun, and M. Baykal-Gursoy, “Probabilities programming models for dispatching and resource allocation in traffic incident management,” 2004, http://ie.rutgers.edu/resource/research_paper/paper_05-025.pdf.
  17. P. Jacko, “Stochastic programming framework for resource allocation and (nearly-) optimal priority rules,” BCAM-EHU/UPV-Robotiker Joint Workshop, 2009.
  18. S. E. Elmaghraby and G. Ramachandra, “Optimal resource allocation in activity networks under stochastic conditions,” 2009, http://www.ise.ncsu.edu/people/faculty/docs/elmaghrabyPaper.pdf.
  19. C. R. Nwozo and C. I. Nkeki, “On dynamic optimization technique for resource allocation problems in a transportation network,” Journal of Mathematical Association of Nigeria, vol. 36, pp. 30–37, 2009. View at Google Scholar
  20. C. I. Nkeki, “The use of dynamic optimization technique for the allocation of buses from different stations to different routes by a transportation company in Nigeria,” International Journal of Computational and Applied Mathematics, vol. 4, pp. 141–152, 2009. View at Google Scholar
  21. C. R. Nwozo and C. I. Nkeki, “On a dynamic optimization technique for resource allocation problems in a production company,” American Journal of Operations Research, vol. 2, pp. 357–363, 2012. View at Publisher · View at Google Scholar
  22. W. B. Powell, Approximate Dynamic Programming For Asset Management, Princeton University Press, Princeton, NJ, USA, 2004.