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Using multistage stochastic optimisation to manage major production incidents

Author

Listed:
  • Margaret Armstrong

    (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Alain Galli

    (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Rija Razanatsimba

    (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

Abstract

A mining company has entered into contractual commitments to supply certain quantities to clients in each time period. The planned production would be sufficient unless major problems that interrupt production occur. To overcome these difficulties, the Company may be able to obtain more of the commodity: from its strategic stockpile (if there is sufficient there), by buying it on the spot market or for some commodities such as gold and uranium, by leasing it. Two sources of uncertainty, the spot price of the commodity and the occurrence of serious production incidents are considered. Multistage stochastic programming with recourse was used to solve this problem, because in addition to providing the dollar value of the project, it gives decision-makers the 'roadmap' to reach the optimal value. A case study over a 5-year period is used to illustrate the proposed procedure.

Suggested Citation

  • Margaret Armstrong & Alain Galli & Rija Razanatsimba, 2012. "Using multistage stochastic optimisation to manage major production incidents," Post-Print hal-00771482, HAL.
    • Handle: RePEc:hal:journl:hal-00771482
    DOI: 10.1179/1743286312Y.0000000010
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    Cited by:

    1. Reus, Lorenzo & Pagnoncelli, Bernardo & Armstrong, Margaret, 2019. "Better management of production incidents in mining using multistage stochastic optimization," Resources Policy, Elsevier, vol. 63(C), pages 1-1.

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