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Author (up) Reus, L.; Pagnoncelli, B.K.; Armstrong, M. doi  openurl
  Title Better management of production incidents in mining using multistage stochastic optimization Type Journal Article
  Year 2019 Publication Resources Policy Abbreviated Journal Resour. Policy  
  Volume 63 Issue Pages 101404  
  Keywords  
  Abstract Mining incidents, Optimal policy, Stochastic dual dynamic programming, Risk-aversion, CVaR, Julia language“,

abstract = ”Among the many sources of uncertainty in mining are production incidents: these can be strikes, environmental issues, accidents, or any kind of event that disrupts production. In this work, we present a strategic mine-planning model that takes into account these types of incidents, as well as random prices. When confronted by production difficulties, mines which have contracts to supply customers have a range of flexibility options including buying on the spot market, or taking material from a stockpile if they have one. Earlier work on this subject was limited in that the optimization could only be carried out for a few stages (up to 5 years) and in that it only analyzed the risk-neutral case. By using decomposition schemes, we are now able to solve large-scale versions of the model efficiently, with a horizon of up to 15 years. We consider decision trees with up to 615 scenarios and implement risk aversion using Conditional Value-at-Risk, thereby detecting its effect on the optimal policy. The results provide a “roadmap” for mine management as to optimal decisions, taking future possibilities into account. We present extensive numerical results using the new sddp.jl library, written in the Julia language, and discuss policy implications of our findings.
 
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0301-4207 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1004  
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