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

• Published in: Annals of operations research Vol. 286; no. 1-2; pp. 583 - 615
• Main Authors: Hinz, Juri, Tarnopolskaya, Tanya, Yee, Jeremy
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2020; Springer; Springer Nature B.V
• Subjects: Algorithms; Business and Management; Combinatorics; Control methods; Data mining; Decision making; Diagnostic systems; Dynamic programming; Mathematical research; Mining industry; Operations research; Operations Research/Decision Theory; Optimal control; Optimization; S.i.: Claio 2016; Stochastic processes; Studies; Theory of Computation; Australia; Optimal switching; Approximate dynamic programming; Markov decision process; Duality; Natural resource extraction; Real option
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-018-2910-3

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.