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Efficient algorithms of pathwise dynamic programming for decision optimization in mining operations

Author

Listed:
  • Juri Hinz

    (University of Technology Sydney)

  • Tanya Tarnopolskaya

    (CSIRO)

  • Jeremy Yee

    (University of Technology Sydney
    CSIRO)

Abstract

Complexity and uncertainty associated with commodity resource valuation and extraction requires stochastic control methods suitable for high dimensional states. Recent progress in duality and trajectory-wise techniques has introduced a variety of fresh ideas to this field with surprising results. This paper presents a concept which implements this promising development and illustrates it on a selection of traditional commodity extraction problems. We describe efficient algorithms for obtaining approximate solutions along with a diagnostic technique, which provides a quantitative measure for solution performance in terms of the distance between the approximate and the optimal control policy. All quantitative tools are efficiently implemented and are publicly available within a user friendly package in the statistical language R, which can help practitioners in a broad range of decision optimization problems.

Suggested Citation

  • Juri Hinz & Tanya Tarnopolskaya & Jeremy Yee, 2020. "Efficient algorithms of pathwise dynamic programming for decision optimization in mining operations," Annals of Operations Research, Springer, vol. 286(1), pages 583-615, March.
    • Handle: RePEc:spr:annopr:v:286:y:2020:i:1:d:10.1007_s10479-018-2910-3
    DOI: 10.1007/s10479-018-2910-3
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    3. Paolo Falbo & Juri Hinz & Piyachat Leelasilapasart & Cristian Pelizzari, 2021. "A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading," JRFM, MDPI, vol. 14(6), pages 1-22, May.
    4. Juri Hinz, 2021. "On Approximate Solutions for Partially Observable Decision Problems," Research Paper Series 421, Quantitative Finance Research Centre, University of Technology, Sydney.

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