The Question

How do you pick the lowest-cost path to an electric mining haul fleet?

Case study · Mining · Fleet electrification

Client work product

Thirty years of trucks, decided nine seconds at a time

Client
Teck Resources Limited
Industry
Mining: copper, zinc, steelmaking coal
Decision type
Capital & Network Strategy (fleet transition)
Horizon
30 years, diesel retirement through full electrification
Options modeled
4 electric truck types, 3 recharging technologies, rail, conveyor
Run time per simulated year
9 seconds, from 160
Optimizer
Reinforcement learning in the loop (Microsoft Project Bonsai)

Client brief

Teck Resources, headquartered in Vancouver, mines copper, zinc, and steelmaking coal and has committed to carbon neutrality across its operations by 2050, with a 33% cut in the carbon intensity of its operations by 2030 on the way. Diesel burned in mobile equipment is the largest source of the company's direct emissions, and the open-pit haul fleet is where most of that diesel goes. The route down runs through a transition rather than a purchase: retire diesel trucks on some schedule, bring in electric trucks of several types, build the charging or trolley infrastructure each type needs, and move some tonnage to rail or conveyor where the pit allows. Every combination carries a different acquisition cost, lifecycle cost, and emissions path over three decades. Teck's technology and innovation team already has a high-fidelity AnyLogic model of the haul operation. One simulated year takes close to three minutes to run, which makes a 30-year scenario an hour and the thousands of scenarios an optimizer needs out of reach.

§ 1

The decision

A fleet transition is a capital sequence. Order and timing decide the outcome: chargers have to exist before the trucks that need them, a truck type that is right in year five can be stranded by year fifteen, and a diesel unit retired early leaves salvage value on the table while one retired late keeps burning fuel against the 2030 target.

A per-truck net present value in a spreadsheet cannot see any of that. Queues at a charging station, the interaction between haul cycle length and battery range, the way a trolley segment changes which routes are worth electrifying: these are behaviors of the whole system, and a spreadsheet row has no place for them.

Teck wanted the combination of four electric truck types, three recharging technologies, rail, and conveyor that reached its emissions targets at the lowest acquisition and lifecycle cost over 30 years, with the diesel retirement rate and the electric introduction rate as inputs its own planners set.

§ 2

Three problems in one project

The pilot had three parts, and the first two existed only to make the third possible.

  • Speed. Cut the existing model's run time to under two minutes per simulated year on average, across a 30-year horizon, without losing the fidelity that made the model credible.
  • Integration. Put an AI optimizer in the loop with the model. Microsoft's Project Bonsai would propose a transition plan, the model would run it and return cost, emissions, and haulage, and the optimizer would learn from the result and propose again.
  • The answer. Run the combinations of truck types, charging technologies, rail, and conveyor against Teck's chosen retirement and introduction rates, and find the plan that minimized lifetime cost while hitting the carbon targets.

§ 3

How the work ran

Teck's technology and innovation team owned the model throughout. SimWell's job was to make it fast enough to optimize and to wire it to the optimizer.

Speed came from subtraction. The team deactivated model logic block by block, measured the effect on run time, and kept only the detail that moved the answer. Truck transport times and counts, the diesel-to-electric transition logic, truck additions and removals, loading and hauling unit capacity, speed, number, and location, and maintenance and downtime all stayed. Detail that cost seconds without changing a decision went.

Integration meant teaching the model to talk. After each iteration the model handed the optimizer four numbers for the plan it had just run: capital cost, operating cost, annual greenhouse gas emissions, and total haulage for the year. The optimizer compared plans on cost outside the model and sent back the next plan to test. Teck selected the scenarios; the loop did the searching.

Exhibit A · A 30-year transition, one iteration at a time
Transition model · Scales illustrative Optimizer iteration 1 of 12 Haul fleet by year Year 1Year 30 Diesel Electric Charger or trolley segment built Emissions, share of today 100%0 Net zero target Lifetime cost, indexed 100 Best plan so far 100 Each run returns CapEx, OpEx, emissions and haulage to the optimizer. One simulated year: 9 s, down from 160 s Each iteration tries a different retirement and build schedule

§ 4

A note on the optimizer

Microsoft retired Project Bonsai in late 2023. The method it served does not depend on the product. A simulation fast enough to act as a training environment lets a reinforcement learning agent try thousands of plans at a cost of seconds each, which is what makes a 30-year, multi-technology capital decision searchable at all. The same loop runs today on current reinforcement learning tooling.

Results

Results

Run time per simulated year fell from 160 seconds to 9 seconds, a 30-year scenario from more than an hour to under five minutes. The integration with the optimizer was validated, with the model returning CapEx, OpEx, annual emissions, and haulage after every iteration. Against Teck's chosen diesel retirement rate and electric introduction rate, the loop returned an optimized count and type of electric trucks and charging stations across the 30-year horizon.

The pilot did what a pilot is for. Teck went on to commission a broader open-pit emissions reduction study on the same foundation, covering alternatives across multiple mines.

Since then, Teck has piloted the first battery-electric truck to haul copper concentrate, between Highland Valley Copper and the rail terminal at Ashcroft, and sold its steelmaking coal business to Glencore in 2024. Teck and Anglo American agreed in September 2025 to merge as Anglo Teck, headquartered in Vancouver, with completion expected between September 2026 and March 2027.

SimWell helps operators make better decisions under uncertainty. We build simulation and optimization models that reflect operational reality, test scenarios before committing resources, and transfer capability so teams can run decisions independently.

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