Conceptual model

Conceptual Model

System Boundary

The model covers one 8-hour ore haulage shift from truck dispatch through loading, loaded travel, crusher dumping, and empty return/re-dispatch to an ore loader. It includes the directed mine road topology, ore loaders, the primary crusher dump point, truck payloads, stochastic service times, stochastic travel-time variation, and finite-capacity road segments.

The model excludes truck breakdowns, refuelling, operator breaks, maintenance dispatch, blasting delays, stockpile blending, downstream plant availability, and detailed traffic rules such as priority control or passing bays.

Entities

• Trucks are active SimPy processes.
• Ore payloads are represented as a truck state and payload tonnage, not as separate entities.

Each truck has a payload capacity, empty speed factor, loaded speed factor, start node, current route, and cycle-time history.

Resources

• Loaders at LOAD_N and LOAD_S, using capacities and service-time parameters from data/loaders.csv.
• Primary crusher dump resource D_CRUSH, using capacity and dump-time parameters from data/dump_points.csv.
• Road segments with capacity below 999, represented as shared SimPy resources. Opposite directed edges with the same two endpoint nodes share one physical road resource, so traffic in both directions competes for a single-lane segment.

Roads with capacity 999 are treated as unconstrained but still have time-consuming travel.

Events

The main truck cycle is:

1. Truck dispatched from its current node.
2. Loader selected by dispatch policy.
3. Truck travels empty over the directed topology to the selected loader.
4. Truck queues for the loader.
5. Loading starts and ends.
6. Truck travels loaded to the crusher.
7. Truck queues for the crusher.
8. Dumping starts and ends.
9. Completed dump contributes payload tonnes to shift throughput.
10. Truck is re-dispatched empty from the crusher while the shift is still active.

For constrained road resources, the event log also records road queue and road entry events.

State Variables

The simulation tracks:

• simulation clock time in minutes
• truck loaded/empty status
• truck current node during dispatch and movement
• selected loader and route
• tonnes delivered to the crusher
• completed truck cycle times
• loader, crusher, and constrained-road queue waits
• loader, crusher, and constrained-road busy time
• truck productive time spent travelling, loading, or dumping

Assumptions Derived From Data

• The primary production objective is ore delivery from LOAD_N and LOAD_S to CRUSH.
• Trucks carry 100 tonnes per completed dump.
• Node and dump/loader service-time means and standard deviations define loading and dumping duration.
• Edge distance and maximum speed define mean travel time.
• Edges marked closed are unavailable for routing.
• Scenario YAML files override the baseline scenario data.

Introduced Assumptions

• Loading and dumping durations are positive truncated normal samples.
• Travel time uses a lognormal multiplier with the configured coefficient of variation.
• Routing uses shortest expected travel time on currently open directed edges.
• Dispatch chooses the loader with the lowest estimated empty travel time, current loader queue workload, mean load time, and loaded travel time to the crusher.
• Trucks are continuously dispatched while the 8-hour shift is active.
• Throughput is counted only for dumping completions before the shift cutoff.
• Truck utilisation in results.csv is productive utilisation: the fraction of shift time spent travelling, loading, or dumping, excluding resource queue waits.

Limitations

• The dispatch policy is heuristic and myopic; it does not globally optimise fleet assignment.
• Road resources are first-come, first-served and do not model traffic direction control or passing bays.
• The narrow main ramp primarily affects startup access from parking in this topology; after trucks enter the upper network, the crusher-to-loader loop usually does not use it.
• Resource service times are independent and identically distributed; temporal correlation from weather, shift conditions, or operator effects is not modelled.
• Unfinished truck cycles at the shift cutoff do not count toward delivered tonnes.

Performance Measures

The experiment reports, by scenario and replication:

• total tonnes delivered to the crusher
• tonnes per hour
• average completed truck cycle time
• average productive truck utilisation
• crusher utilisation
• average loader queue time
• average crusher queue time
• loader-specific utilisation and queue waits
• constrained-road utilisation and queue waits

Scenario summaries report means and 95% confidence intervals for throughput metrics across 30 replications.

README

Synthetic Mine Throughput Simulation

This submission implements a SimPy discrete-event simulation for the synthetic mine haulage benchmark. It reads the provided topology and scenario files, applies scenario overrides, runs 30 replications per required scenario, and writes reproducible machine-readable outputs.

Install

Use Python 3 and install the listed dependencies:

python3 -m pip install -r requirements.txt

The tested local environment already had simpy, pandas, numpy, networkx, pyyaml, and scipy available.

Run

Run all required scenarios from this folder:

python3 simulate.py

This writes:

• results.csv: one row per scenario replication
• summary.json: scenario-level means, 95% confidence intervals, assumptions, limitations, and bottleneck summaries
• event_log.csv: event trace for truck dispatch, road movement, loading, and dumping

To run a subset:

python3 simulate.py --scenarios baseline trucks_12

Model Summary

Trucks are SimPy processes. Loaders, the crusher, and finite-capacity road segments are SimPy resources. Travel uses shortest expected time routes over the directed graph, then applies stochastic travel variation. Loading and dumping times are positive truncated normal samples. Roads with capacity below 999 are constrained resources; opposite directed edges with the same endpoints share one physical road resource.

Dispatch selects the loader with the lowest estimated empty travel time, current loader workload, mean loading time, and loaded travel time to the crusher. Trucks continue cycling while the shift clock is active. Ore throughput is counted only when a dump completes before the 480-minute cutoff.

See conceptual_model.md for the full conceptual model, assumptions, state variables, and limitations.

Key Results

All values below are means across 30 replications of an 8-hour shift.

Operational Questions

1. Baseline expected throughput is about 12,130 tonnes per 8-hour shift, or 1,516 tonnes per hour.

2. The likely steady-state bottlenecks are the crusher, the south loader L_S, the single-lane south face access road road:J6-LOAD_S, and the crusher approach road:CRUSH-J4. In the baseline, crusher utilisation is 0.889 and L_S utilisation is 0.874.

3. Adding trucks helps but saturates. Reducing to 4 trucks lowers throughput to about 7,847 tonnes. Increasing from 8 to 12 trucks raises throughput only to about 12,757 tonnes, a gain of about 5.2%, while average cycle time rises from 30.64 to 43.13 minutes and crusher queue time rises to 11.60 minutes.

4. Improving the narrow main ramp does not materially improve shift throughput in this topology. The ramp upgrade case increases mean throughput by only about 33 tonnes, or 0.3%. The event log shows the main ramp creates startup queueing from parking, but the recurring pit-crusher loop mostly stays on the upper network.

5. Throughput is highly sensitive to crusher service time. Doubling mean crusher dump time from 3.5 to 7.0 minutes reduces throughput to about 6,447 tonnes, a drop of about 46.9%, and crusher queue time rises to 27.67 minutes.

6. Losing the main ramp has a small operational impact under this model because traffic can reroute through the bypass and the recurring production loop does not rely on the lower-to-upper ramp after startup. Mean throughput falls to about 11,987 tonnes, a drop of about 1.2%.

Additional Scenario Suggested

A useful next scenario would test a second or faster south-pit loader. The current results suggest extra trucks quickly push queueing to the crusher and south-side loading path; a loader-side change would help determine whether the south loader or crusher is the better next capital target.

Limitations

• No breakdowns, refuelling, operator breaks, blasting delays, or maintenance events are modelled.
• Dispatch is a simple queue-aware heuristic, not a global optimiser.
• Road capacity resources are first-come, first-served and do not include passing bays or directional control logic.
• The model counts only completed dumps by shift end; partially completed cycles do not contribute tonnes.
• The main ramp result depends on the supplied topology: pits and crusher are connected on the upper network, so the ramp is not a repeated haul-cycle link.

Reviewer form

Reviewer Form: Synthetic Mine Throughput

Submission: 2026-04-25__001_synthetic_mine_throughput__codex-cli__gpt-5-5__xhigh Reviewer: Independent human reviewer (opus subagent) Date: 2026-04-27

Automated report

• Automated report file: results/evaluation_report.json
• Automated checks: 53/53 passed (100%)
• Required scenarios present: yes (all 6, 30 replications each)
• Behavioural checks passed: all 6 (trucks_12 > trucks_4, baseline > trucks_4, ramp_upgrade >= baseline, crusher_slowdown < baseline, ramp_closed <= baseline, saturation plausible)
• Python LOC: 753 code lines (1 file)
• Token usage method: not reported in evaluation_report.json (submission.yaml records 503k tokens, 400s wall time)

Human quality score

Top 3 strengths

1. Faithful, complete SimPy implementation. Truck cycles, loader/crusher/road resources, queue tracking, and post-cutoff exclusion of partial cycles are all done correctly. The shared physical road resource for opposite-direction edges is a thoughtful modelling choice that goes beyond a literal reading of the data.
2. Excellent traceability. The event log is comprehensive (10 event types, ~350k rows, queue lengths captured) and the bottleneck ranking in summary.json is derived from per-resource utilisation/queue metrics rather than asserted.
3. Honest, evidence-led interpretation. The agent correctly identifies that the ramp scenarios are largely cosmetic in this topology (because the haul loop bypasses the ramp), and quantifies the crusher as the dominant bottleneck — both of which match the structure of the data.

Top 3 concerns or gaps

1. No warm-up discussion. The scoring guide explicitly asks for warm-up justification; warmup_minutes: 0 is silently inherited from baseline.yaml and never addressed. With trucks starting at PARK, there is genuine startup transient (visible in road_enter events), so this matters.
2. Monolithic 863-line single file. Rubric and Python style guide prefer many small files. While the code is internally well-organised, a model.py / experiment.py / report.py split would be more reproducible/maintainable.
3. Additional scenario proposed but not run. loader_upgrade_south is described in summary.json but never actually executed. Since the prompt explicitly invites one optional scenario, running it would have strengthened the value-of-information argument substantially.

Failure modes observed

• None of the listed failure modes apply substantively. Truck utilisation definition is non-standard (excludes queue waits) but is documented in the conceptual model.

Final recommendation

Strong submission. Final score 85/100. This is a competent, traceable, defensible discrete-event simulation that would be trusted as a first-pass decision-support artefact, with the caveat that a reviewer should double-check the warm-up choice and the productive-utilisation definition before quoting numbers externally.