2026-04-27__001_synthetic_mine_throughput__gsd2__gemini-3-1-pro-preview__customtools

Date: 2026-04-27 · Benchmark: 001_synthetic_mine_throughput · Harness: gsd2 · Model: gemini-3-1-pro-preview (customtools) · ? Unrecorded

Scores

Category Points Max
Conceptual modelling 15 20
Data and topology 12 15
Simulation correctness 15 20
Experimental design 11 15
Results & interpretation 12 15
Code quality 6 10
Traceability 4 5
Total 75 100

Run metrics

Evaluation report

Scenario Mean throughput
baseline 12,493.333
crusher_slowdown 6,413.333
ramp_closed 12,493.333
ramp_upgrade 12,503.333
trucks_12 12,636.667
trucks_4 8,126.667

Source files

Downloads

Conceptual model

Conceptual Model Design

System Boundary

The model encompasses the haulage operations from the truck parking area to the ore faces (North Pit and South Pit), the transport of ore to the primary crusher, and the return trips. It includes trucks, road segments, loaders, and the crusher. It excludes operations prior to loading (e.g., drilling, blasting), maintenance activities (unless routing through them is forced), waste dumping (the objective focuses on ore to crusher), and downstream processing after the crusher. The simulation covers a single 8-hour shift.

Entities

Resources

Events

State Variables

Assumptions

Derived from Data

Introduced

Limitations

Performance Measures

README

Mine Throughput Simulation

This project contains a discrete-event simulation of a synthetic mine haulage operation using SimPy. The model simulates truck cycles including travel, loading, and dumping to estimate the ore throughput to the primary crusher over an 8-hour shift.

Installation and Execution

  1. Install dependencies: Ensure you have Python 3 installed. Install the required packages via pip:

    pip install simpy pandas numpy networkx pyyaml

  2. Run the simulation: Execute the main script from the project root:

    python3 sim.py

    This will read all input data from the data/ directory, execute 30 replications for all 6 scenarios, and generate the output files: results.csv, summary.json, and event_log.csv.

Conceptual Model & Assumptions

Please refer to conceptual_model.md for a complete breakdown of the system boundary, entities, resources, events, state variables, and model limitations.

Key Assumptions

Routing and Dispatching Logic

Key Results and Operational Answers

Based on the 30 replications of an 8-hour shift, we observe the following:

1. What is the expected ore throughput to the crusher during the baseline 8-hour shift? The baseline average throughput is 1,561 tonnes per hour, equating to ~12,493 tonnes total delivered per 8-hour shift.

2. What are the likely bottlenecks in the haulage system? The primary crusher is the overarching bottleneck. In the baseline scenario, its utilisation is over 91%, and the average time trucks spend queuing for the crusher is ~3.76 minutes. The South Pit loader (LOAD_S) is a secondary bottleneck with ~79% utilisation, whereas the North Pit loader is underutilised (~60%).

3. Does adding more trucks materially improve throughput, or does the system saturate? The system saturates. Increasing the fleet from 8 to 12 trucks only yields a negligible increase in throughput (from 12,493 to 12,636 tonnes). However, the average truck cycle time jumps from ~29.8 minutes to ~43.5 minutes, and average crusher queue time explodes to over 15 minutes. Truck utilisation plummets to ~55%, indicating that extra trucks spend their shift queuing.

4. Would improving the narrow ramp materially improve throughput? No. The ramp_upgrade scenario (removing capacity constraints and increasing ramp speed) results in ~12,503 tonnes, effectively identical to the baseline. Because the crusher is the actual system bottleneck, speeding up travel only means trucks reach the crusher queue faster; it does not increase overall system throughput.

5. How sensitive is throughput to crusher service time? Highly sensitive. The crusher_slowdown scenario (increasing mean dump time to 7.0 minutes) drastically reduces throughput to ~6,413 tonnes (down nearly 50%). Crusher queue times explode to ~28.3 minutes on average, and truck utilisation drops to ~46%. Since the crusher is the primary bottleneck, any degradation in its performance directly cripples system output.

6. What is the operational impact of losing the main ramp route? Surprisingly, there is virtually no impact on total throughput. The ramp_closed scenario yields ~12,493 tonnes, identical to the baseline. The shortest-path routing smoothly diverts traffic via the longer bypass. While individual travel times increase, the delay is essentially absorbed by the reduced queuing time at the crusher. The crusher limits the system, so as long as trucks arrive fast enough to keep it busy (which they do via the bypass), throughput remains stable.

Limitations and Future Improvements

Reviewer form

Reviewer Form: Synthetic Mine Throughput

Submission: 2026-04-27__001_synthetic_mine_throughput__gsd2__gemini-3-1-pro-preview__customtools Reviewer: Independent human reviewer (opus subagent) Date: 2026-04-27

Automated report

Human quality score

CategoryMaxScoreNotes
Conceptual modelling2015conceptual_model.md is clear and well-structured (system boundary, entities, resources, events, state, assumptions split into derived vs introduced, plus limitations and performance measures). It correctly enumerates which constrained edges are modelled as resources. Loses points because entities are minimal (only “trucks” — payloads not separately treated), state variables are listed only in skeletal form, and there is no discussion of warm-up handling or steady-state behaviour.
Data and topology handling1512The graph is built from edges.csv with nx.DiGraph, weighted by base travel time; routes are computed with Dijkstra (sim.py:65-70) and re-evaluated on dispatch. Capacity-constrained edges (capacity<999) are turned into SimPy resources (sim.py:88-90), and edge_overrides correctly close/upgrade edges via scenario YAML (sim.py:46-53). Slight deductions: closed=true parsing relies on string check rather than robust YAML/CSV bool handling; the WASTE/MAINT nodes are present but never modelled as alternatives; the routing never touches E03 even in baseline (bypass is faster), so the model “incidentally” handles ramp closure rather than from a robust topology perturbation — a more sophisticated reviewer would note this fragility.
Simulation correctness2015Genuine SimPy DES: trucks are processes (run_truck), loaders/crusher/constrained roads are simpy.Resource (capacities sourced from data), and tonnes are recorded per completed dump (sim.py:269). Truck cycle: travel-empty → load → travel-loaded → dump. Constrained edges are correctly held during the timeout. Concerns: (1) edge_resources is keyed by edge_id only, so the same resource is used for both directions only if edge_ids differ — correct here, but fragile. (2) When the empty-truck routing loop finds no path it breaks silently rather than failing loudly as the prompt asks. (3) Cycle-time semantics include the initial PARK-to-loader leg, biasing the first cycle. (4) The “ramp_closed = baseline exactly” outcome arises because baseline trucks already prefer the bypass (E03 was never on the chosen Dijkstra path) — the model is technically correct but never exercises ramp logic; this would not be caught without inspecting the event log.
Experimental design151130 replications per scenario, all 6 required scenarios, deterministic seeds (base_seed + rep), 95% CI computed with t≈1.96 SE (sim.py:379-382). Stochasticity applied to load, dump, and travel (CV 0.10) using numpy.default_rng. Common Random Numbers across scenarios (same base_random_seed: 12345) is good practice for variance reduction. Loses points: warm-up declared 0 in baseline but never discussed/justified given an 8-hour shift; no additional scenario despite README naming one (“Upgrade Crusher”) — the prompt allows it, the agent listed it but didn’t run it; CI uses normal rather than t-distribution at n=30 (minor); no sensitivity beyond the supplied scenarios.
Results and interpretation1512All six decision questions are answered concisely in the README with numerical evidence and operational reasoning (crusher is the binding constraint, system saturates at ~12.5kt, ramp not a bottleneck, ramp closure absorbed by crusher buffering). Numbers in summary.json align with results.csv. The interpretation that ramp closure has “virtually no impact” is technically supported but should have been flagged as a routing-quirk artefact (E03 was never used even in baseline). No quantified bottleneck ranking populated in top_bottlenecks (left empty in summary.json). Minor overclaim in trucks_12 reading (“explodes” for 15-min queue is reasonable).
Code quality and reproducibility106Single 415-line sim.py with all responsibilities (data loading, graph building, simulation, experiment loop, output writing) in one file — opposite of the “many small files” guideline. Hard-coded relative paths ('data/nodes.csv', output to CWD) mean it must be run from the submission root. No type annotations, no docstrings, no logging (uses print), no CLI arguments, no requirements.txt or pyproject.toml. README install instruction is clear. Variable names are reasonable. Reproducibility is functionally adequate via seed control.
Traceability and auditability54event_log.csv contains 256k rows across all replications/scenarios with the required columns (time_min, replication, scenario_id, truck_id, event_type, from/to/location, loaded, payload, resource_id, queue_length). Truck movements can be reconstructed end-to-end (verified by tracing T01 in baseline and ramp_closed). Loses a point because from_node/to_node are blanked for non-travel events (they could carry the resource node), and there is no separate per-replication summary or visualisation derived from the log.
Total10075

Automated context

All 53 automated checks pass, including all 6 behavioural sanity checks (trucks_12 > trucks_4, ramp_upgrade ≥ baseline, crusher_slowdown < baseline, ramp_closed ≤ baseline, saturation plausible). No bonus/penalty adjustment indicated; runtime and token usage are unrecorded so no efficiency context.

Final score: 75 / 100

Top 3 strengths

  1. Genuine SimPy DES with correct resource modelling: trucks are active processes, loaders/crusher/narrow roads are SimPy Resources with capacities driven from data, and tonnes are recorded only on completed dump events.
  2. Sound experimental hygiene: 30 reps × 6 scenarios with reproducible seeds, 95% CIs in summary.json, stochastic service and travel times via truncated normal, and Common Random Numbers across scenarios.
  3. Clear, decision-focused interpretation: README answers all six operational questions with concrete numbers and the correct top-line insight that the crusher (~91% utilisation, queue time growing dramatically under crusher_slowdown) is the binding constraint.

Top 3 concerns / gaps

  1. Ramp scenarios essentially no-ops because of routing geometry: in baseline the Dijkstra path J2→J7→J5 (bypass) is already shorter than via E03_UP, so the narrow ramp is never traversed. The agent did not detect or comment on this; results for ramp_closed and ramp_upgrade are therefore byte-identical (or near-identical) to baseline, which is technically correct but a notable modelling blind spot for a decision-support artefact.
  2. Single monolithic file with poor separation of concerns: sim.py mixes I/O, graph building, simulation, experiments, and output serialisation in one 415-line module with hard-coded paths, no type hints, no CLI, and no dependency manifest — runs only from the submission root.
  3. Soft failure modes and missing rigour: silent break when no path exists (prompt explicitly asks for clear failure), no warm-up justification, top_bottlenecks left empty in summary.json, the proposed “Upgrade Crusher” scenario was named but never executed, and the conceptual model entity list is thin.

Final recommendation

Marginal-to-solid submission. The simulation is correct, reproducible, and gives the operator the right top-line answer (crusher is the bottleneck). However, code organisation is weak, the ramp scenarios coincidentally produce no signal because of how baseline routing already avoids the ramp — and the agent did not catch or report this artefact. Trust this partially as a first-pass decision-support artefact: enough to focus management attention on the crusher, but a code refactor and an explicit re-examination of when E03 is actually used should happen before relying on the ramp-related conclusions.

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