scholarly journals Production scheduling with uncertain supply: a new solution to the open pit mining problem

2012 ◽  
Vol 14 (2) ◽  
pp. 361-380 ◽  
Author(s):  
Salih Ramazan ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Production Scheduling ◽  
Open Pit ◽  
Open Pit Mining ◽  
Uncertain Supply

scholarly journals AUTOMATIC MUCK PILE CHARACTERIZATION FROM UAV IMAGES

2019 ◽  
Vol IV-2/W5 ◽  
pp. 163-170 ◽  
Author(s):  
F. Schenk ◽  
A. Tscharf ◽  
G. Mayer ◽  
F. Fraundorfer
Keyword(s):  
Production Scheduling ◽  
Multiple Scales ◽  
Expert Knowledge ◽  
User Interaction ◽  
Open Pit ◽  
Open Pit Mining ◽  
Accurate Information ◽  
Unseen Data ◽  
Bounding Boxes ◽  
Uav Images

<p><strong>Abstract.</strong> In open pit mining it is essential for processing and production scheduling to receive fast and accurate information about the fragmentation of a muck pile after a blast. In this work, we propose a novel machine-learning method that characterizes the muck pile directly from UAV images. In contrast to state-of-the-art approaches, that require heavy user interaction, expert knowledge and careful threshold settings, our method works fully automatically. We compute segmentation masks, bounding boxes and confidence values for each individual fragment in the muck pile on multiple scales to generate a globally consistent segmentation. Additionally, we recorded lab and real-world images to generate our own dataset for training the network. Our method shows very promising quantitative and qualitative results in all our experiments. Further, the results clearly indicate that our method generalizes to previously unseen data.</p>

scholarly journals Joint stochastic short-term production scheduling and fleet management optimization for mining complexes

2020 ◽  
Vol 21 (4) ◽  
pp. 1717-1743
Author(s):  
Christian Both ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Stochastic Optimization ◽  
Production Scheduling ◽  
Planning Horizon ◽  
Open Pit ◽  
Open Pit Mining ◽  
Fleet Management ◽  
Production Schedule ◽  
Short Term ◽  
Term Extraction ◽  
Management Optimization

Abstract This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.

Production Scheduling for Strategic Open Pit Mine Planning: A Mixed-Integer Programming Approach

2020 ◽  
Vol 68 (5) ◽  
pp. 1425-1444 ◽  
Author(s):  
Orlando Rivera Letelier ◽  
Daniel Espinoza ◽  
Marcos Goycoolea ◽  
Eduardo Moreno ◽  
Gonzalo Muñoz
Keyword(s):  
Integer Programming ◽  
Production Scheduling ◽  
Large Scale ◽  
Mine Planning ◽  
Open Pit ◽  
Mixed Integer ◽  
Open Pit Mining ◽  
Programming Approach ◽  
Programming Methodology ◽  
Scale Optimization

Production scheduling is a large-scale optimization problem that must be solved on a yearly basis by every open pit mining project throughout the world. Surprisingly, however, this problem has only recently started to receive much attention from the operations research community. In this article, O. Rivera, D. Espinoza, M. Goycoolea, E. Moreno, and G. Muñoz propose an integer programming methodology for tackling this problem that combines new classes of preprocessing schemes, cutting planes, heuristics, and branching mechanisms. This methodology is shown to compute near-optimal solutions on a number of real-world planning problems whose complexity is beyond the capabilities of preexisting approaches.

Reliability-based performance analysis of mining drilling operations through Markov chain Monte Carlo and mean reverting process simulations

SIMULATION ◽  
2020 ◽  
Vol 96 (7) ◽  
pp. 593-604 ◽  
Author(s):  
Omer Faruk Ugurlu ◽  
Mustafa Kumral
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Production Scheduling ◽  
Commodity Prices ◽  
Mining Operation ◽  
Open Pit ◽  
Open Pit Mining ◽  
Mining Operations ◽  
Rotary Drilling

In recent years, commodity prices have swiftly decreased, narrowing the profit margin for many mining operations and forcing them to find effective cost management strategies to respond to low prices. Given that equipment is one of the most significant assets of a mining company, efficient equipment utilization has strong potential to reduce costs. This paper focuses on the relationship between the number of available drilling machines based on reliability analysis and the number of holes to be created on a bench of an open pit mining operation. Since equipment availability is random in nature, a range of holes to be drilled corresponding to a specified probability level was determined. To assess the performance of the proposed approach, a case study was carried out using two stochastic modeling techniques. Evolutions of reliabilities of 10 rotary drilling machines over a specific time were simulated by Markov chain Monte Carlo and mean reverting processes, using historical data. Multiple simulations were then used for risk quantification. Results show that the proposed approach can be used as a tool to assist production scheduling and assess the associated risk.

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