Journal of Coal Science and Engineering (China)

, Volume 18, Issue 3, pp 313–319 | Cite as

Mining truck scheduling with stochastic maintenance cost

Article

Abstract

Open pit mining operations utilize large scale and expensive equipment. For the mines implementing shovel and truck operation system, trucks constitute a large portion of these equipment and are used for hauling the mined materials. In order to have sustainable and viable operation, these equipment need to be utilized efficiently with minimum operating cost. Maintenance cost is a significant proportion of the overall operating costs. The maintenance cost of a truck changes non-linearly depending on the type, age and truck types. A new approach based on stochastic integer programming (SIP) techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over the life of mine (multi-year time horizon) to minimize maintenance cost.

The maintenance cost data in mining usually has uncertainty caused from the variability of the operational conditions at mines. To estimate the cost, usually historic data from different operations for new mines, and/or the historic data at the operating mines are used. However, maintenance cost varies depending on road conditions, age of equipment and many other local conditions at an operation. Traditional models aim to estimate the maintenance cost as a deterministic single value and financial evaluations are based on the estimated value. However, it does not provide a confidence on the estimate. The proposed model in this study assumes the truck maintenance cost is a stochastic parameter due to the significant level of uncertainty in the data and schedules the available fleet to meet the annual production targets. The scheduling has been performed by applying both the proposed stochastic and deterministic approaches. The approach provides a distribution for the maintenance cost of the optimized equipment schedule minimizing the cost.

Keywords

mining truck scheduling mixed integer programming minimizing stochastic cost mining equipment 

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Copyright information

© The Editorial Office of Journal of Coal Science and Engineering (China) and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Mining Engineering Department, Western Australia School of MinesCurtin University of TechnologyKalgoorlieAustralia
  2. 2.Anglogold AshantiPerthAustralia

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