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Title: Discrete-Event Simulation of Payload Variance Effects on Truck Bunching
Organization

BHP - The University of Queensland

Period

July 2013 - June 2015

Status

Completed

Est. Budget

USD 0.6M

Est. Business Benefit

USD 3.0M

Indicative Value-to-cost

5.5x

Keywords

Discrete-event simulation, truck bunching, payload variance, haulage productivity, cycle time, Talpac

Benefit basis

Estimated value from improved haulage cycle efficiency, lower queuing time, lower fuel consumption, and better dispatch/planning decisions.

Business Challenges 
​Payload variance in surface mining creates uneven haul-truck performance. Heavily loaded trucks travel more slowly on ramps than lightly loaded trucks, and faster trucks can be constrained by slower trucks, creating bunching, queues, cycle-time variability, production loss, increased fuel consumption, and downstream instability. The business needed a way to quantify the productivity and cost impacts of payload variance and truck bunching before operational changes were implemented.
Expanded Technical Solution
The project developed a discrete-event simulation algorithm and user-friendly software to estimate how payload variance affects truck bunching, productivity, cycle time, fuel consumption, greenhouse gas emissions, and cost. The model represented haulage as a sequence of stochastic events including loading, travelling, ramp speed variation, queueing, dumping, and return travel. It was designed to complement existing mine planning tools such as Talpac by adding a dynamic bunching layer that traditional deterministic models may not capture.
Technical Work Packages/Methods
  • Created a discrete-event simulation model for shovel-truck haulage cycles with stochastic payload, travel speed, loading time, dumping time, queueing, and route constraints.
  • Implemented truck interaction logic to represent faster trucks being delayed by slower trucks on ramps and constrained haul-road segments.
  • Linked payload variance to speed, fuel use, cycle time, cost, and emissions impacts.
  • Validated the algorithm using real site datasets and compared outputs with expected haulage performance indicators.
  • Developed a user-facing software tool for scenario testing and sensitivity analysis.
Key Deliverables
  • Discrete-event simulation algorithm for truck bunching.
  • User-friendly truck bunching software package.
  • Validated model using real surface-mining datasets.
  • Scenario-analysis capability for payload policy, fleet dispatch, ramp management, and cost/emissions impacts.
Results and Value Created
The project provided a practical analytical tool for quantifying a problem that is difficult to see in static planning models. It helped estimate the operational cost of payload variability and enabled mine planners to test changes in loading control, dispatch strategy, and haul-road management. The estimated benefit reflects improved fleet productivity, reduced fuel use from avoidable queuing/bunching, and reduced production losses caused by cycle-time instability.
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