# Default configuration for T-ALNS-RRD Reproduction
# All parameters aligned with the original paper.

# Problem scale (paper §4.2)
problem:
  n_customers: 47
  n_vehicles: 4
  depot_count: 1
  vehicle_capacity_kg: 120
  service_time_min: 4
  area_width_km: 8.0
  area_height_km: 10.0
  operating_start: 360    # 6:00 AM in minutes (0 = midnight)
  operating_end: 1080     # 6:00 PM in minutes
  n_time_intervals: 12    # H = 12 one-hour intervals

# Customer generation (paper §4.2)
customers:
  demand_min_kg: 3
  demand_max_kg: 12
  time_window_categories:
    morning:
      earliest: 540      # 9:00
      latest: 720        # 12:00
    afternoon:
      earliest: 780      # 13:00
      latest: 960        # 16:00
    evening:
      earliest: 1020     # 17:00
      latest: 1200       # 20:00
  num_clusters: 3
  cluster_labels: ["residential", "commercial", "office"]

# Road network (paper §3.2)
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          # base travel time noise
  use_complete_graph: true

# Traffic congestion (paper §3.2, Table in §4.3)
traffic:
  # hourly congestion multipliers (6:00-7:00, ..., 17:00-18:00)
  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: 1.0     # θ in Eq.9
  risk_aversion_beta: 0.3          # β in Eq.10
  uncertainty_base: 0.05           # base uncertainty proportion of travel time

# Cost function weights (paper Eq.1)
cost:
  lambda_lateness: 1.0            # λ₁
  lambda_congestion: 1.0          # λ₂
  lambda_stability: 0.3           # λ₃ (for RRD only)

# ALNS parameters (paper §3.3.1)
alns:
  max_iterations: 1000
  time_limit_sec: 600
  destroy_ratio_min: 0.1          # α_min
  destroy_ratio_max: 0.4          # α_max
  initial_temperature_factor: 0.05  # T₀ = factor × Z(S⁰)
  cooling_rate: 0.99975            # γ in Eq.21
  reaction_factor: 0.1             # ξ in Eq.19
  segment_length: 100              # π iterations for weight update
  stall_limit: 200                 # T_stall consecutive no-improvement
  max_attempts: 5                  # max attempts per candidate generation
  # Reward levels (σ₁, σ₂, σ₃, σ₄)
  reward_global_best: 1.0
  reward_improvement: 0.5
  reward_accepted: 0.2
  reward_rejected: 0.0

# Tabu memory parameters (paper §3.3.2)
tabu:
  move_tabu:
    tenure: 7                     # τ_move initial
    tenure_min: 3
    tenure_max: 12
    overlap_threshold: 0.5        # μ in Eq.23
    stall_for_increase: 50        # τ_stall for adaptive tenure
  solution_tabu:
    tenure: 15                    # τ_sol
    buffer_size: 1000
    hash_prime: 1000000007
  frequency:
    normalization_factor: 2       # κ in Eq.33
    normalization_interval: 50    # ν in Eq.33
  diversification:
    delta_max: 0.7                # δ_max in Eq.27
    eta_balance: 0.5              # η in Eq.28
    weights: [0.4, 0.3, 0.3]     # ω₁, ω₂, ω₃ in Eq.27
  aspiration:
    beta_threshold: 0.3           # β in Eq.30
    gamma_threshold: 0.8          # γ in Eq.31

# RRD parameters (paper §3.3.3)
rrd:
  rollout:
    horizon_min_min: 30           # H_min
    horizon_max_min: 120          # H_max
    urgency_alpha: 1.0            # α_urgency
    n_sim_min: 2                  # N_min
    n_sim_max: 50                 # N_max
    mc_iterations: 50             # Monte Carlo iterations
    time_overhead_ms: 10
    time_per_sim_ms: 50
  dispatch:
    # Weights for composite score (Eq.40)
    weight_rollout: 0.4           # ω₁
    weight_stability: 0.3         # ω₂
    weight_recovery: 0.3          # ω₃
  tabu:
    penalty: 50.0                 # τ_penalty in Eq.39
    bonus: 25.0                   # τ_bonus in Eq.39
  events:
    urgency_threshold: 0.5        # trigger threshold
    # Event weights (α_e, β_e, γ_e in Eq.35)
    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               # |A_e| ≤ 20

# Experiment settings (paper §4.8, §5)
experiments:
  random_seeds: 30          # 30 runs per configuration
  seed_start: 1             # seed range: 1..30
  report_mean_std: true
  # Sensitivity analysis ranges (paper §5.1)
  sensitivity:
    fleet_sizes: [2, 3, 4, 5, 6]
    customer_counts: [30, 40, 47, 60]
    capacities: [80, 100, 120, 140, 160]
  # Uncertainty levels (paper §5.2)
  robustness:
    sigma_values: [0.1, 0.2, 0.3, 0.5]