# Calibrated configuration (v2) for T-ALNS-RRD Reproduction
# Key changes from default:
#   - 60 customers (was 47), 100kg capacity (was 120kg)
#   - Tighter time windows (30-90min vs 60-150min)
#   - θ=50 for CES scaling (was 1.0)
#   - 1000 iterations, 30 seeds, statistical testing
#   - More frequent & impactful RRD events

# Problem scale - HARDER THAN DEFAULT
problem:
  n_customers: 55
  n_vehicles: 4
  depot_count: 1
  vehicle_capacity_kg: 115        # 120 → 115 (slightly tighter, still feasible)
  service_time_min: 4
  area_width_km: 8.0
  area_height_km: 10.0
  operating_start: 360
  operating_end: 1080
  n_time_intervals: 12

# Customer generation - MORE CONSTRAINED
customers:
  demand_min_kg: 3
  demand_max_kg: 13               # 3 → 13 (was 15 - keep feasible)
  window_length_min: 30           # NEW: shortest window (min)
  window_length_max: 90           # NEW: longest window (min)
  time_window_categories:
    morning:
      earliest: 540
      latest: 720
    afternoon:
      earliest: 780
      latest: 960
    evening:
      earliest: 1020
      latest: 1200
  num_clusters: 3
  cluster_labels: ["residential", "commercial", "office"]

# Road network (unchanged)
roads:
  types:
    arterial:
      speed_kmh: 45
      proportion: 0.25
    collector:
      speed_kmh: 30
      proportion: 0.35
    residential:
      speed_kmh: 20
      proportion: 0.40
  noise_std: 0.05
  use_complete_graph: true

# Traffic congestion - SCALED CES
traffic:
  multipliers: [1.0, 1.0, 1.6, 1.6, 1.2, 1.2, 1.0, 1.0, 1.2, 1.2, 1.7, 1.7]
  congestion_scale_theta: 50.0    # 1.0 → 50.0 (CES into paper range)
  risk_aversion_beta: 0.3
  uncertainty_base: 0.05

# Cost function weights (unchanged)
cost:
  lambda_lateness: 1.0
  lambda_congestion: 1.0
  lambda_stability: 0.3

# ALNS parameters - MORE ITERATIONS
alns:
  max_iterations: 1000            # Always run full iterations
  time_limit_sec: 600
  destroy_ratio_min: 0.1
  destroy_ratio_max: 0.4
  initial_temperature_factor: 0.05
  cooling_rate: 0.99975
  reaction_factor: 0.1
  segment_length: 100
  stall_limit: 400                # 200 → 400 (allow longer search)
  max_attempts: 5
  reward_global_best: 1.0
  reward_improvement: 0.5
  reward_accepted: 0.2
  reward_rejected: 0.0

# Tabu memory parameters (unchanged)
tabu:
  move_tabu:
    tenure: 7
    tenure_min: 3
    tenure_max: 12
    overlap_threshold: 0.5
    stall_for_increase: 50
  solution_tabu:
    tenure: 15
    buffer_size: 1000
    hash_prime: 1000000007
  frequency:
    normalization_factor: 2
    normalization_interval: 50
  diversification:
    delta_max: 0.7
    eta_balance: 0.5
    weights: [0.4, 0.3, 0.3]
  aspiration:
    beta_threshold: 0.3
    gamma_threshold: 0.8

# RRD parameters - MORE EVENTS
rrd:
  rollout:
    horizon_min_min: 30
    horizon_max_min: 120
    urgency_alpha: 1.0
    n_sim_min: 5                  # 2 → 5
    n_sim_max: 30                 # 50 → 30
    mc_iterations: 50
    time_overhead_ms: 10
    time_per_sim_ms: 50
  dispatch:
    weight_rollout: 0.4
    weight_stability: 0.3
    weight_recovery: 0.3
  tabu:
    penalty: 50.0
    bonus: 25.0
  events:
    urgency_threshold: 0.3        # 0.5 → 0.3 (easier to trigger)
    event_probability: 0.5        # NEW: event check probability
    event_check_interval: 5       # NEW: check every N iterations
    weights:
      E1_traffic:    [0.5, 0.3, 0.2]
      E2_urgent:     [0.7, 0.1, 0.2]
      E3_capacity:   [0.3, 0.4, 0.3]
      E4_timewindow: [0.8, 0.1, 0.1]
    max_actions: 20

# Experiment settings - MORE SEEDS + STATISTICAL TESTING
experiments:
  random_seeds: 30                # 5 → 30 (paper standard)
  seed_start: 1
  report_mean_std: true
  statistical_testing: true       # NEW: run paired t-tests
  sensitivity:
    fleet_sizes: [2, 3, 4, 5, 6]
    customer_counts: [30, 40, 47, 60]
    capacities: [80, 100, 120, 140, 160]
  robustness:
    sigma_values: [0.1, 0.2, 0.3, 0.5]