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Accelerating the convergence of value iteration by using partial transition functions

Author

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  • Arruda, Edilson F.
  • Ourique, Fabrício O.
  • LaCombe, Jason
  • Almudevar, Anthony

Abstract

This work proposes an algorithm that makes use of partial information to improve the convergence properties of the value iteration algorithm in terms of the overall computational complexity. The algorithm iterates on a series of increasingly refined approximate models that converges to the true model according to an optimal linear rate, which coincides with the convergence rate of the original value iteration algorithm. The paper investigates the properties of the proposed algorithm and features a series of switchover queue examples which illustrates the efficacy of the method.

Suggested Citation

  • Arruda, Edilson F. & Ourique, Fabrício O. & LaCombe, Jason & Almudevar, Anthony, 2013. "Accelerating the convergence of value iteration by using partial transition functions," European Journal of Operational Research, Elsevier, vol. 229(1), pages 190-198.
    • Handle: RePEc:eee:ejores:v:229:y:2013:i:1:p:190-198
    DOI: 10.1016/j.ejor.2013.02.029
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    References listed on IDEAS

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    1. He, Miao & Zhao, Lei & Powell, Warren B., 2012. "Approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation," European Journal of Operational Research, Elsevier, vol. 222(2), pages 328-340.
    2. Hao, Tang & Lei, Zhou & Tamio, Arai, 2008. "Optimization of a special case of continuous-time Markov decision processes with compact action set," European Journal of Operational Research, Elsevier, vol. 187(1), pages 113-119, May.
    3. Arruda, E.F. & do Val, J.B.R., 2008. "Stability and optimality of a multi-product production and storage system under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 188(2), pages 406-427, July.
    4. Sauré, Antoine & Patrick, Jonathan & Tyldesley, Scott & Puterman, Martin L., 2012. "Dynamic multi-appointment patient scheduling for radiation therapy," European Journal of Operational Research, Elsevier, vol. 223(2), pages 573-584.
    5. Arruda, E.F. & Fragoso, M.D. & do Val, J.B.R., 2011. "Approximate dynamic programming via direct search in the space of value function approximations," European Journal of Operational Research, Elsevier, vol. 211(2), pages 343-351, June.
    6. Wanda Rosa-Hatko & Eldon Gunn, 1997. "Queues with switchover - A review and critique," Annals of Operations Research, Springer, vol. 69(0), pages 299-322, January.
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    Cited by:

    1. Arruda, E.F. & Fragoso, M.D., 2015. "Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm," European Journal of Operational Research, Elsevier, vol. 240(3), pages 697-705.

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