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Open pit mine production scheduling (OPMPS) is a decision problem which maximizes net present value (NPV) by determining the extraction time and destination of each block of ore and/or waste in a deposit. Stockpiles can be used to maintain low-grade ore for future processing, to store extracted material until processing capacity is available, or to blend material based on single or multiple block characteristics (i.e., metal grade and/or contaminant).
We adapt an existing integer-linear program to an operational polymetallic mine.
The stockpile is used to blend materials based on multiple block characteristics.
We search systematically for an optimal grade combination.
Our proposed solution technique rapidly provides schedules for large instances.
Related schedules exhibit significantly different stockpiling and material flows.
We study a Lagrangian decomposition algorithm recently proposed by Dan Bienstock and Mark Zuckerberg for solving the LP relaxation of a class of open pit mine project scheduling problems.SEE MORE
In this paper, we consider an underground production scheduling problem consisting of determining the proper time interval or intervals in which to complete each mining activity so as to maximize a mine's discounted value while adhering to precedence, activity durations, and production and processing limits. We present two different integer programming formulations for modeling this optimization problem. Both formulations possess a resource-constrained project scheduling problem structure. The first formulation uses a fine time discretization and is better suited for tactical mine scheduling applications. The second formulation, which uses a coarser time discretization, is better suited for strategic scheduling applications. We illustrate the strengths and weaknesses of each formulation with examples.SEE MORE
• We propose new linear models for modeling stockpiles in open pit mining.
• We compare how their assumptions affect solution quality and tractability.
• These models include blending requirements without unrealistic assumptions.
• Experiments show that our proposed models are tractable and yield good approximations.
In this article we describe a mixed integer programming (MIP) formulation with which this last step can be optimized, and compare it to Whittle’s Milawa algorithm.SEE MORE
We propose an automatic primal-dual aggregation scheme to exactly solve special structured linear programming (LP) problems with a very large number of scenarios. The algorithm aggregates scenarios and constraints in order to solve a smaller problem, which is automatically disaggregated using the information of its dual variables. Extensive computational experiments are performed on portfolio and general LP instances.SEE MORE
In this article we study a well-known integer programming formulation of the problem that we refer to as C-PIT. We propose a new decomposition method for solving the linear programming relaxation (LP) of C-PIT when there is a single capacity constraint per time period.SEE MORE
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