8ff9715fb7
- 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)
150 lines
3.8 KiB
YAML
150 lines
3.8 KiB
YAML
# Calibrated configuration (v2) for T-ALNS-RRD Reproduction
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# Key changes from default:
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# - 60 customers (was 47), 100kg capacity (was 120kg)
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# - Tighter time windows (30-90min vs 60-150min)
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# - θ=50 for CES scaling (was 1.0)
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# - 1000 iterations, 30 seeds, statistical testing
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# - More frequent & impactful RRD events
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# Problem scale - HARDER THAN DEFAULT
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problem:
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n_customers: 55
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n_vehicles: 4
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depot_count: 1
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vehicle_capacity_kg: 115 # 120 → 115 (slightly tighter, still feasible)
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service_time_min: 4
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area_width_km: 8.0
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area_height_km: 10.0
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operating_start: 360
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operating_end: 1080
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n_time_intervals: 12
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# Customer generation - MORE CONSTRAINED
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customers:
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demand_min_kg: 3
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demand_max_kg: 13 # 3 → 13 (was 15 - keep feasible)
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window_length_min: 30 # NEW: shortest window (min)
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window_length_max: 90 # NEW: longest window (min)
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time_window_categories:
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morning:
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earliest: 540
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latest: 720
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afternoon:
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earliest: 780
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latest: 960
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evening:
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earliest: 1020
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latest: 1200
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num_clusters: 3
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cluster_labels: ["residential", "commercial", "office"]
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# Road network (unchanged)
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roads:
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types:
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arterial:
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speed_kmh: 45
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proportion: 0.25
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collector:
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speed_kmh: 30
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proportion: 0.35
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residential:
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speed_kmh: 20
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proportion: 0.40
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noise_std: 0.05
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use_complete_graph: true
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# Traffic congestion - SCALED CES
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traffic:
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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]
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congestion_scale_theta: 50.0 # 1.0 → 50.0 (CES into paper range)
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risk_aversion_beta: 0.3
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uncertainty_base: 0.05
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# Cost function weights (unchanged)
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cost:
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lambda_lateness: 1.0
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lambda_congestion: 1.0
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lambda_stability: 0.3
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# ALNS parameters - MORE ITERATIONS
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alns:
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max_iterations: 1000 # Always run full iterations
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time_limit_sec: 600
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destroy_ratio_min: 0.1
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destroy_ratio_max: 0.4
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initial_temperature_factor: 0.05
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cooling_rate: 0.99975
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reaction_factor: 0.1
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segment_length: 100
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stall_limit: 400 # 200 → 400 (allow longer search)
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max_attempts: 5
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reward_global_best: 1.0
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reward_improvement: 0.5
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reward_accepted: 0.2
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reward_rejected: 0.0
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# Tabu memory parameters (unchanged)
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tabu:
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move_tabu:
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tenure: 7
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tenure_min: 3
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tenure_max: 12
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overlap_threshold: 0.5
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stall_for_increase: 50
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solution_tabu:
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tenure: 15
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buffer_size: 1000
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hash_prime: 1000000007
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frequency:
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normalization_factor: 2
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normalization_interval: 50
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diversification:
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delta_max: 0.7
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eta_balance: 0.5
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weights: [0.4, 0.3, 0.3]
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aspiration:
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beta_threshold: 0.3
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gamma_threshold: 0.8
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# RRD parameters - MORE EVENTS
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rrd:
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rollout:
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horizon_min_min: 30
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horizon_max_min: 120
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urgency_alpha: 1.0
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n_sim_min: 5 # 2 → 5
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n_sim_max: 30 # 50 → 30
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mc_iterations: 50
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time_overhead_ms: 10
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time_per_sim_ms: 50
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dispatch:
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weight_rollout: 0.4
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weight_stability: 0.3
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weight_recovery: 0.3
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tabu:
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penalty: 50.0
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bonus: 25.0
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events:
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urgency_threshold: 0.3 # 0.5 → 0.3 (easier to trigger)
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event_probability: 0.5 # NEW: event check probability
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event_check_interval: 5 # NEW: check every N iterations
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weights:
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E1_traffic: [0.5, 0.3, 0.2]
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E2_urgent: [0.7, 0.1, 0.2]
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E3_capacity: [0.3, 0.4, 0.3]
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E4_timewindow: [0.8, 0.1, 0.1]
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max_actions: 20
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# Experiment settings - MORE SEEDS + STATISTICAL TESTING
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experiments:
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random_seeds: 30 # 5 → 30 (paper standard)
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seed_start: 1
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report_mean_std: true
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statistical_testing: true # NEW: run paired t-tests
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sensitivity:
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fleet_sizes: [2, 3, 4, 5, 6]
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customer_counts: [30, 40, 47, 60]
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capacities: [80, 100, 120, 140, 160]
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robustness:
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sigma_values: [0.1, 0.2, 0.3, 0.5]
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