The biggest concern our client faced was that in many sections of the road there was no room for vehicles to pass each other, especially the larger mining trucks. The project required the development of specialised software with the ability to learn the best routes and starting times for their entire fleet, maximising vehicle usage per shift, while at the same time minimising the risk of vehicle incidents and therefore, increasing the profitability of the company.

      

Mining Vehicle

      

The scope of the project was to provide software which could be used to determine the expected output tonnage produced at the mine given the composition of the fleet, mine topology and various other specific parameters of the mine, while also providing enough versatility to enable the software to readily ‘self-perform’ analyses of different scenarios, allowing application of the software to different mine sites.

Our developed software has:

    • The ability to model the relevant characteristics of the mine, including:
      • Source material location/s
      • Destination for the material
      • Key locations in the haulage route including passing bays, stockpile turnins (another form of passing bay, also referred to as ‘reversing bays’) and intersections (which connect different haulage routes and also can act as passing bays)
      • Gradient and length of all segments of the haulage route/s
    • Ability to define haulage routes within the mine model
    • Ability to define different fleets and attach them to haulage routes
      • Fleets may consist of a range of predefined truck types based on manufacturers’ data, with the ability to add “custom” truck types (e.g. in the case where the haulage capacity of a particular truck model may be “downrated” due to material properties at a particular mine source location)
    • Ability to define ‘mine specific’ parameters including:
      • Shift duration
      • Shift non-productive time (e.g. due to prestarts, breaks etc.)
      • Parameters for accounting for truck mechanical availability, hotseating, number and trip frequency of Light Vehicle trips per shift, number and trip frequency for “nonproduction output” related heavy vehicles
    • Ability for the user to readily interpret and adjust all problem configuration parameters
    • A simulation which models all of the above and produces the expected output tonnage and other required data
    • Reporting of results including a list of KPIs and the key simulation parameters used to determine the results
  • Ability to export summary and detailed output to Comma Separated Values (CSV) file.
      

To simplify the problem, we considered a basic version of the mine topology that provided most of the variables from the full problem. This initial simplification easily allowed us to find a general solution that fit the requirement of the mine, as well as any similarly structured mines.