495 The study of dispatch systems traces its origins back to the start of the Mining Industry 3.0 in the 1970’s when the introduction of computerized automation was first tested in open pit mines to enhance efficiency and reduce streamline costs. Starting with the implementation of White & Olson’s work (1986) as a baseline methodology, the development of multiple approaches and strategies to address fleet-related complexities has advanced significantly over the last 45 years. All the research work is now compiled in the existing Fleet Management Systems (FMS), that integrate in a single specialized software all the routines required for the control of a mine fleet, including functioning algorithms, status reports, data transfer, and many other ancillary services. In the context of the complexities of a mining operation, the degree of uncertainty is considerable across all aspects of the process. This uncertainty in the data and orebody models creates an accuracy risk, which can lead to incorrect production plans that, ultimately, leads to generating multiple scenarios upon which to decide. To simplify matters, mine planners rely on known information, from benchmark parameters to nominal data. Therefore, most production planning calculations are made based on average values, including fleet management estimations. Even with similar truck characteristics, the fleet performance can be chaotic once exposed to a simulation analysis (Ahumada et al., 2020). Contrary to long-term schedules, short-term plans are often more sensitive to these changes and, unless they are properly assessed and timescaled (Levinson & Dimitrakopoulos, 2023), the impact of variable information may compromise the attainability of the daily plans. This uncertainty has different degrees of criticality, depending on the process under evaluation, from the accuracy of the reserve model (Kumar & Dimitrakopoulos, 2021) to the performance of the machinery (Moradi-Afrapoli & Askari-Nasab, 2019), machinery breakdowns (Paduraru & Dimitrakopoulos, 2019), economic conditions (Pirbalouti & Askari-Nasab, 2023), primary crusher availability (Bakhtavar & Mahmoudi, 2018), or even weather cycles and environmental difficulties where applicable. The consequences of choosing to include or exclude the uncertainty factor in the operational plans are directly correlated with a risk management strategy. Previous work in stochastic optimization for fleet management in mining activities includes multiple approaches to different aspects of the problem. Nevertheless, most of these approaches are limited in scope, including truck and shovel short-term decisions as part of the scheduling optimization. Villalba & Dimitrakopoulos (2016) presented an assessment on short-term planning and fleet utilization under different degrees of uncertainty to generate well-informed production schedules. Moradi-Afrapoli and Askari-Nasab (2018) reviewed the impact of stochasticity when evaluating plant feed rate, shovel utilization, and truck queuing, observing some improvements in the behavior of the fleet. The topic of geological and equipment uncertainty was later reviewed by Quigley & Dimitrakopoulos (2020), incorporating the shovel sequencing and truck performance under unplanned events, and was the first to note the complexity hurdles for commercial solvers when handling large case studies. One common problem observed in most approaches is the disconnection of the simultaneous optimization between shovel utilization, truck utilization, and plant feed rate, which was assessed by Moradi-Afrapoli and Askari-Nasab (2020) against the performance of commercial software Modular Mining DISPATCH®, identifying several shortcomings in the optimization process for the current commercial implementations. Other optimization approaches that include shovel allocation and sequencing (Both & Dimitrakopoulos, 2020, and De Carvalho & Dimitrakopoulos, 2023) reinforce learning applied to truck tasks (De Carvalho & Dimitrakopoulos, 2021), dynamic dispatching policy selection (Mirzaei-Nasirabad et al., 2023), and grade control to update diglines (De Carvalho & Dimitrakopoulos, 2024). Considering the advances made up to this point, the next step is to analyze the likelihood of changes occurring in the initial plan in response to individual and combined uncertainties. This entails identifying differences from a baseline plan when uncertainty impacts upper-level (shovel allocation) and lower-level (truck dispatching) decisions for a variety of equally-probable scenarios. The formulation of the problem must also consider the possibility of parameter reselection, depending on the complexity of the problem, similar to the parametric-dependent policies suggested by Paduraru & Dimitrakopoulos (2018), such as material recategorization (ore/waste) and destination changes (to process, to store, or to dispose). Existing commercial implementations are bounded by the limitations of the conventional deterministic calculations, the computational cost associated with the size and complexity of the problem, and the degree to which any decisions rendered can actually be implemented. Therefore, they can only
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