Simulation Model Reveals Strategies to Increase Efficiencies of Haulage Network

Mining Method




The Customer

The customer operates one of the largest deep open-pit mines in the world.

The Challenges

The mine operates a complex haulage network with a large fleet of haul trucks and shovels operating with different loading rates at various locations in the open pit. For the average truck cycle, queuing at the various locations result in significant delays over time. For an operation of this size, these haulage delays were causing a decrease in productivity, which is why identifying and addressing the sources of operational delays was paramount.

Delays arising from truck breakdowns, maintenance, and variations in shovel performance, were causing haulage trucks to spend hours in queues unnecessarily. The client understood that minimising queuing offered an opportunity to increase productivity and felt they had a potential solution by utilising stockpiles. However, trialling such a solution would be risky and incur substantial costs given the operation's size. The question brought to Polymathian was whether the transition's effect on productivity would justify the costs.

The Solution

The simulation team modelled the complexity of the entire mine using discrete event simulation. The model quantified the intricate, variable, and interrelated components of the site. This granular attention to detail allowed Polymathian to provide a representative model which reflected the behaviour of each component. The model identified sources of haulage inefficiency and through numerous iterations of scenario testing, we arrived at the end solution for our client.

According to our model, the client increased total output by a staggering 10% by modifying truck to shovel allocation procedures and new strategies for the use of tactical stockpiles. The increased productivity was due to the alleviation of queues at the crusher and the ability to handle higher peaks of crusher demand. Further, these strategies gave the client greater control of their ore variability. We identified a solution for the client through comprehensive modelling without exposing them to unnecessary risks.

Decision Support

The simulation model helped answer business critical questions such as:

  • How should allocation logic for trucks and shovel change to reduce truck queuing?
  • To what degree does a reduction in queuing translate to increased throughput?
  • What would be the operating costs of using stockpiles?
  • Could active selection of certain trucks utilising tactical stockpiles serve twofold by also reducing ore variability?

Learn how we can help you make better decisions with simulation today.