Optimising value chains from Mine-to-Market

# Artificial Intelligence vs. Product Planning Through Decomposition

of integrated mine planning

The mathematical complexity of integrated mine planning prevents tools such as Excel Solver from solving this class of problems in its entirely. Some coal operations utilise an educated deconstruction of the planning problem to enable optimisation tools like Excel Solver to be applied in a sequential or iterative manner.

Due to significant price differences between products, it might be assumed that maximising production of the highest price product first, and then iterating product planning from highest to lowest sale price, would correspond with an approximately optimal production plan. This is one example of how operations have sometimes used product price to deconstruct the planning process.

Following this logic, a marketing team used Excel Solver to generate a result similar to that labelled as “Heuristic” in Table 2. This planning procedure creates a large amount of P1 product; the product with the highest sale price in Table 1. Optimising the same problem in its entirety, BlendOpt generated the result shown in Table 2.

Table 1. Selected details of three thermal coal products sold at a single mine.

Table 2. Integrated mine planning results comparing BlendOpt with existing planning procedures.

The average revenue per tonne is slightly lower in the BlendOpt plan, however BlendOpt generates significantly more total product tonnes with a plan revenue increase of approximately 5% compared to the original plan. It is worth noting that only a relatively small amount of P1 was created in the BlendOpt result, while a substantially larger amount of the lower priced P2 was produced. It should also be noted that the scope of this project included contracted sales for P1 of 520kt, the exact amount of P1 produced by BlendOpt. Removal of the P1 contract further reduces P1 within the BlendOpt result.

The Excel Solver plan produces lower financial results because of the lower yields that were necessary to maximise production of the highest valued low-ash thermal product. If products are optimised simultaneously, the higher revenue per tonne is more than compensated for through higher total production.

BlendOpt – Smarter Integrated Production
Planning
from Mine to Market

BlendOpt’s mathematical optimisation, meta-heuristic search algorithms and Artificial Intelligence can optimise against many non-trivial financial metrics associated with the integrated mining supply chain planning problem, in comparison to human intuition, Excel Solver, and rule-based techniques.

The magnitude of quantitative differences reported in the example above are generally repeatable for multi-seam/multi-mine and multi-product operations.

When using mathematical optimisation software, it’s important the software can model those problem features that alter the mathematical relationship between decision variables and the financial objectives optimised; BlendOpt is capable of addressing this important requirement, and we saw examples where problem features from seemingly unrelated components of the mining supply chain had unexpected consequences on the financially optimal mine plan.

For instance, price penalties were found to significantly alter the mathematically optimal product mix in one operation while a more comprehensive cost model was found to significantly alter the financially optimal mining plan at another operation.

Given that many planning exercises make at least some of the assumptions reviewed here, the findings presented in this paper raise interesting questions regarding the magnitude of value that could be lost from current planning procedures.

In today’s dynamic market, easy-to-use integrated mine planning software is becoming a valuable tool for competitive advantage. Some coal producers are already considering these possibilities as unprecedented pricing regimes create conditions in which historical intuition provides less meaningful guidance.