Initial commit: T-ALNS-RRD paper reproduction project

- Paper: Optimizing urban last mile delivery efficiency (Liu & Wang, 2025)
- 5 algorithms: Static-VRPTW, TA-Greedy, ALNS-Base, T-ALNS, T-ALNS-RRD
- v1 baseline + v2 calibrated experiments with full results
- Tabu memory ablation study with convergence analysis
- Comprehensive final report (FINAL_REPORT.md)

t_alns_rrd_reproduction/results/calibrated_tabu/config_used.yaml

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+# 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]

t_alns_rrd_reproduction/results/calibrated_tabu/tables/tabu_ablation.csv

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+configuration,total_cost_mean,total_cost_std,otdr_mean,otdr_std,ces_mean,ces_std,computation_time_mean,iterations_mean
+ALNS-Base (no Tabu),3213.5472600000003,92.47828809019447,86.54545454545455,3.302891295379083,873.8731200000002,72.66381047359131,83.00211868286132,656.4
++ Move Tabu only,3232.33156,23.85173277307108,86.18181818181817,3.042400096487549,884.6135199999999,23.691658934148084,51.74106793403625,472.8
++ Frequency Memory only,3216.9874600000003,87.42641474241665,87.63636363636364,3.726163914894401,892.23014,36.74464052488473,67.69878606796264,532.6
+Full T-ALNS,3207.3305,83.1845531049185,83.63636363636363,2.8747978728803405,859.0403399999999,44.94860445801399,56.30859026908875,499.4