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

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. Th...

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Veröffentlicht in:Annals of operations research Jg. 286; H. 1-2; S. 583 - 615
Hauptverfasser: Hinz, Juri, Tarnopolskaya, Tanya, Yee, Jeremy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2020
Springer
Springer Nature B.V
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-018-2910-3