1. The theory behind the technology
Now you have practiced for a while, it is time to get deep into the theory behind the DBS (Direct Block Scheduling) technology.
1.1. MiningMath Uniqueness
MiningMath optimization is not constrained by arbitrary decisions for cut-off grades or pushbacks. Such decisions are usually guided by previous knowledge or automated trial-and-error. Thus, each set of constraints has the potential to deliver an entirely new project development, including economic, technical, and socio-environmental indicators along with a mine schedule while aiming to maximize the NPV of the project.
License to operate is the main risk for the mining industry in 2019-20, according to EY. Any mining company willing to be a market leader should be committed to incorporating socio-environmental aspects into a strategic evaluation, quantifying possibilities, and impacts to discuss with society. This is only possible by bringing mathematical optimization into the decision-making process.
1.2. What is Direct Block Scheduling?
During decades, the mining industry has dealt with Mine Planning as a step-by-step process. This traditional technology has been established in an intelligent manner in the face of the technological limitations of that time. The conventional methodology, portrayed in Figure 1, in general, consists of three main stages: pit optimization with nested pits, using Lerchs-Grossman (LG) algorithm, definition of pushbacks, and scheduling of benches within pushbacks. Intermediate manipulations and cycles over these steps might be required so that a higher Net Present Value (NPV) could be achieved.
Direct Block Scheduling (DBS) is an alternative to this step-wise process. It has been studied for almost 50 years by researchers worldwide, but back then computers were not developed enough to handle the approach that was first proposed by Johnson in 1968. Over the decades, other authors followed Johnson's proposal and introduced their algorithms, technology advanced as well, but the capability to solve larger problems kept being a challenge.
DBS became feasible only after the advent of 64-bits technology and, in 2015, MiningMath DBS, implemented with DBS technology, was officially released into the market. The DBS approach considers all periods simultaneously, providing a holistic view of the mine scheduling problem by maximizing the NPV unconstrained to predefined pushbacks & cut-offs. Figure 2 summarizes DBS's studies over the last decades.
The objective of DBS is to define the pit limit and mine schedule simultaneously, that is, to determine which blocks should be mined, when this should happen and to where we should send it to maximize the NPV, while respecting production and operational constraints, slope angles, discount rate, stockpiles, among others, all performed straight from the block model. This means that the steps of pit optimization, pushbacks, and scheduling are not obtained separately, but in a single and optimized process.
In addition, the algorithm's framework, based on mixed-integer linear programming (MILP) with heuristics, is flexible to include any sort of other constraints (fleet and excavation hours, metal production, average haul distance, among others) and blending. Figure 3 illustrates a comparison between DBS and traditional methodology.
Direct Block Scheduling does not require to predefine your destinations as it is capable of performing the ore/waste delineation automatically. Because of this optimized definition, the economic values are calculated before importing the data for each possibility. This represents that N different destinations can be created, leaving for the algorithm the duty to define the best blocks’ destinations based on the feasibility to mine them and their economic contributions, represented by the block value. The user no longer needs to assume a certain cut off-grade based on previous experience to predefine whether a block is an ore or waste.
The average grade reported in each period by DBS (Figure 4) can be interpreted as an “optimal” cut-off that was achieved as a consequence of a complex optimization process that considered production, geotechnical and temporal constraints, of which oppose to the assumed fixed arbitrary value that would govern the blocks' destinations, present in the conventional methodology for mine scheduling. Figure 5 illustrates a simplified flowchart of how blocks' destinations are defined.
1.3. Discounted Cashflow x Undiscounted Cashflow
The use LG/pseudoflow methods to perform pit optimization aims to maximize the undiscounted cash flow of the project. On the other hand, MiningMath maximizes the discounted cash flow, including all the constraints that you might be considering in your case. Therefore, regions MiningMath in which has decided not to mine are, probably, regions where you have to pay for removing waste in the earlier periods, but the profit obtained by the discounted revenue from the hidden ore does not pay for the extraction.
A proper comparison between this methodology could be done if you import the final pit surface you have obtained from the other mining package into MiningMath and use it as Force/Restrict mining so that MiningMath will do the schedule optimization using exactly the same surface. This way, you will be able to compare the NPV for each case.
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A license to operate is the risk for mining, according to EY. Meanwhile, a Life Cycle Sustainability Assessment is an unprecedented approach, only possible with Global Optimization, to help mine managers to quantify and evaluate socio-environmental aspects .
A New Approach to Schedule Optimization? — Matthew Randall
Waste Dump Sequencing with SimSched — Karol Bartsch
Innovation and Technology to Improve Open Pit Mine Plan and Design Optimisation — Dr. Luiz Martinez
MORALES, N.; JÉLVEZ, E.; NANCEL, P.; MARINHO, A.; GUIMARÃES, O. A, (2015). A Comparison of Conventional and Direct Block Scheduling Methods for Open Pit Mine Production Scheduling. Proc. APCOM 2015, Alaska.