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

Juri Hinz (), Tanya Tarnopolskaya and Jeremy Yee ()
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Juri Hinz: University of Technology Sydney
Tanya Tarnopolskaya: CSIRO
Jeremy Yee: University of Technology Sydney

Annals of Operations Research, 2020, vol. 286, issue 1, No 25, 583-615

Abstract: 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.

Keywords: Approximate dynamic programming; Duality; Markov decision process; Natural resource extraction; Optimal switching; Real option (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10479-018-2910-3

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