2. METHOD The methodology described herein combines short-term shovel allocation optimization, extraction sequencing, and truck dispatch decisions, integrating two commonly separated optimization models in a single simultaneous analysis. The stochastic modelling primarily focuses on the uncertainty in the input parameters related to the geological risk (grade uncertainty). However, the stochastic analysis is also extended to operational parameters that impact lower-stage decisions related to truck tasks. 2.1.Stochastic Formulation The optimization model designs the open pit mine as a set of locations subdivided in recently blasted work benches represented as muck piles ∈ (mining areas) and destinations ∈ . In this system, a set of shovels ∈ make use of trucks ∈ to transfer material through the mine. Muck piles are sequenced for extraction so that each truck performs an ∈ number of trips (back and forth) in each period ∈ℙ as part of the dynamic dispatch process. The stochastic nature is represented by a set of scenarios ∈ ℂ, encompassing the data distribution and uncertainty of the muck piles’ combined grades. The details of the model follow. 2.1.1.Objective Function and Uncertainty Policies The methodology evaluates an objective function that focuses on the maximization of the overall revenue of the fleet operation, material transportation, and processing activities. = 1 ‖ ℂ‖ ∑ ∑( , ) ∈ ℂ ∈ℙ − 1 ‖ ℂ‖ ∑ ∑( , + , ) ∈ ℂ ∈ℙ (1) As shown in Equation 1, the objective function is established around the profit created from the difference between the gross revenue from the sales of the processed material and the sum of all operating costs, processing costs, and the economic penalties generated from not meeting the tonnage and grade targets, for all periods ∈ℙ (shifts) under analysis. The calculation of the optimization components is given as follows, disaggregating each subcomponent to the corresponding parameters and decision variables associated. In case of the overall cost, it is composed of truck-related, shovel-related, and processing-related costs. , =∑( , , ∗ ) ∈ ∀ ∈ ℂ, ∈ℙ (2)
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