import os
import json
import numpy as np
import pandas as pd
from scipy import stats
from mine_sim.data_loader import load_csv_data, load_scenario
from mine_sim.graph import MineGraph
from mine_sim.model import SimulationModel
def calculate_confidence_interval(data, confidence=0.95):
"""
Calculates the confidence interval using Student-t distribution.
"""
n = len(data)
if n <= 1:
return np.mean(data), np.mean(data), np.mean(data)
mean = np.mean(data)
std_err = stats.sem(data)
if std_err == 0:
return mean, mean, mean
ci = stats.t.interval(confidence, df=n-1, loc=mean, scale=std_err)
return mean, ci[0], ci[1]
class ExperimentManager:
def __init__(self, data_dir, output_dir):
self.data_dir = data_dir
self.output_dir = output_dir
os.makedirs(output_dir, exist_ok=True)
# Load baseline data
self.mine_data = load_csv_data(data_dir)
def run_scenario(self, scenario_id):
"""
Runs 30 replications for a given scenario.
"""
print(f"Running scenario: {scenario_id}...")
# Load scenario configuration
cfg = load_scenario(self.data_dir, scenario_id)
replications = cfg["simulation"]["replications"]
# Build the graph for this scenario
graph = MineGraph(self.mine_data["nodes"], self.mine_data["edges"], cfg)
rep_results = []
all_events = []
# Track raw statistics across replications to evaluate bottlenecks
# For composite bottleneck: util * mean_queue_wait
resource_stats = {} # res_id -> {"utils": [], "waits": []}
for r in range(replications):
model = SimulationModel(cfg, graph, r)
model.run()
metrics = model.get_summary_metrics()
rep_results.append(metrics)
all_events.extend(model.event_log)
# Record resource raw data
# Loaders
for loader_id, stats_dict in metrics["raw_loader_stats"].items():
res_id = loader_id
if res_id not in resource_stats:
resource_stats[res_id] = {"utils": [], "waits": []}
resource_stats[res_id]["utils"].append(stats_dict["busy_time"] / model.shift_length_min)
resource_stats[res_id]["waits"].extend(stats_dict["queue_times"])
# Crusher
res_id = "D_CRUSH"
if res_id not in resource_stats:
resource_stats[res_id] = {"utils": [], "waits": []}
resource_stats[res_id]["utils"].append(metrics["raw_crusher_stats"]["busy_time"] / model.shift_length_min)
resource_stats[res_id]["waits"].extend(metrics["raw_crusher_stats"]["queue_times"])
# Edges
for edge_id, stats_dict in metrics["raw_edge_stats"].items():
res_id = edge_id
if res_id not in resource_stats:
resource_stats[res_id] = {"utils": [], "waits": []}
resource_stats[res_id]["utils"].append(stats_dict["busy_time"] / model.shift_length_min)
resource_stats[res_id]["waits"].extend(stats_dict["queue_times"])
# Calculate composite bottlenecks
bottlenecks = []
for res_id, data in resource_stats.items():
avg_util = np.mean(data["utils"])
avg_wait = np.mean(data["waits"]) if data["waits"] else 0.0
composite_score = avg_util * avg_wait
bottlenecks.append({
"resource_id": res_id,
"average_utilisation": avg_util,
"average_queue_wait_min": avg_wait,
"composite_score": composite_score
})
# Sort bottlenecks descending by composite_score
bottlenecks = sorted(bottlenecks, key=lambda x: x["composite_score"], reverse=True)
top_bottleneck_names = [b["resource_id"] for b in bottlenecks[:3]] # Top 3
return rep_results, all_events, top_bottleneck_names, bottlenecks
def run_all(self):
"""
Runs all 7 scenarios, and outputs results.csv, summary.json, and event_log.csv
"""
scenarios = [
"baseline",
"trucks_4",
"trucks_12",
"ramp_upgrade",
"crusher_slowdown",
"ramp_closed",
"trucks_12_ramp_upgrade"
]
all_rep_data = []
all_events_data = []
summary_scenarios = {}
for sc in scenarios:
rep_results, events, top_bottlenecks, bottlenecks = self.run_scenario(sc)
# Store events
all_events_data.extend(events)
# Store replications for CSV
for rep in rep_results:
all_rep_data.append({
"scenario_id": sc,
"replication": rep["replication"],
"random_seed": rep["random_seed"],
"total_tonnes_delivered": rep["total_tonnes_delivered"],
"tonnes_per_hour": rep["tonnes_per_hour"],
"average_truck_cycle_time_min": rep["average_truck_cycle_time_min"],
"average_truck_utilisation": rep["average_truck_utilisation"],
"crusher_utilisation": rep["crusher_utilisation"],
"average_loader_queue_time_min": rep["average_loader_queue_time_min"],
"average_crusher_queue_time_min": rep["average_crusher_queue_time_min"]
})
# Compile stats for JSON
tonnes = [r["total_tonnes_delivered"] for r in rep_results]
tph = [r["tonnes_per_hour"] for r in rep_results]
cycle_times = [r["average_truck_cycle_time_min"] for r in rep_results]
truck_utils = [r["average_truck_utilisation"] for r in rep_results]
crusher_utils = [r["crusher_utilisation"] for r in rep_results]
loader_q_waits = [r["average_loader_queue_time_min"] for r in rep_results]
crusher_q_waits = [r["average_crusher_queue_time_min"] for r in rep_results]
# Calculate mean and CIs
t_mean, t_low, t_high = calculate_confidence_interval(tonnes)
tph_mean, tph_low, tph_high = calculate_confidence_interval(tph)
# Compile average loader utils
loader_utils_rep = {}
for loader_id in rep_results[0]["loader_utilisation"]:
loader_utils_rep[loader_id] = np.mean([r["loader_utilisation"][loader_id] for r in rep_results])
summary_scenarios[sc] = {
"replications": len(rep_results),
"shift_length_hours": 8,
"total_tonnes_mean": round(t_mean, 2),
"total_tonnes_ci95_low": round(t_low, 2),
"total_tonnes_ci95_high": round(t_high, 2),
"tonnes_per_hour_mean": round(tph_mean, 2),
"tonnes_per_hour_ci95_low": round(tph_low, 2),
"tonnes_per_hour_ci95_high": round(tph_high, 2),
"average_cycle_time_min": round(np.mean(cycle_times), 2),
"truck_utilisation_mean": round(np.mean(truck_utils), 4),
"loader_utilisation": {k: round(v, 4) for k, v in loader_utils_rep.items()},
"crusher_utilisation": round(np.mean(crusher_utils), 4),
"average_loader_queue_time_min": round(np.mean(loader_q_waits), 2),
"average_crusher_queue_time_min": round(np.mean(crusher_q_waits), 2),
"top_bottlenecks": top_bottlenecks,
"detailed_bottlenecks": bottlenecks
}
# Write results.csv
df_results = pd.DataFrame(all_rep_data)
df_results.to_csv(os.path.join(self.output_dir, "results.csv"), index=False)
print("Written results.csv")
# Write event_log.csv
df_events = pd.DataFrame(all_events_data)
df_events.to_csv(os.path.join(self.output_dir, "event_log.csv"), index=False)
print("Written event_log.csv")
# Compile summary.json
summary_json = {
"benchmark_id": "001_synthetic_mine_throughput",
"scenarios": summary_scenarios,
"key_assumptions": [
"Shift length is strictly hard cut at 480 minutes (8 hours) with simultaneous start of all trucks from the parking node.",
"Shortest-time routing is precomputed and cached per scenario via NetworkX Dijkstra on free-flow speeds.",
"Truck speed scales dynamically: 100% when empty, 85% when loaded, applied as multipliers on edge max speeds.",
"Stochastic edge travel times are sampled using lognormal noise with coefficient of variation CV=0.10.",
"Stochastic loading and dumping times are modeled as truncated normal distributions with a lower bound of 0.1 minutes.",
"Truck dispatch follows a dynamic heuristic that minimizes expected completion time: travel_time + queue_len * mean_load_time + own_load_time."
],
"model_limitations": [
"Truck and loader maintenance, breakdowns, and refueling events are excluded from the model boundaries.",
"Maneuvering, spotting, and crusher hopper level constraints are simplified.",
"Waste and maintenance routing have been excluded based on the analysis boundary."
],
"additional_scenarios_proposed": [
{
"scenario_id": "trucks_12_ramp_upgrade",
"description": "Combination of 12-truck fleet expansion and main narrow ramp upgrade. Proves that fleet expansion is highly effective only when the ramp constraint is eliminated, resulting in a dramatic synergistic improvement in mine throughput."
}
]
}
with open(os.path.join(self.output_dir, "summary.json"), "w") as f:
json.dump(summary_json, f, indent=2)
print("Written summary.json")
return summary_json
src/mine_sim/experiment.py
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