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)
146 lines
4.5 KiB
YAML
146 lines
4.5 KiB
YAML
# Default configuration for T-ALNS-RRD Reproduction
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# All parameters aligned with the original paper.
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# Problem scale (paper §4.2)
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problem:
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n_customers: 47
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n_vehicles: 4
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depot_count: 1
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vehicle_capacity_kg: 120
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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 # 6:00 AM in minutes (0 = midnight)
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operating_end: 1080 # 6:00 PM in minutes
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n_time_intervals: 12 # H = 12 one-hour intervals
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# Customer generation (paper §4.2)
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customers:
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demand_min_kg: 3
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demand_max_kg: 12
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time_window_categories:
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morning:
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earliest: 540 # 9:00
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latest: 720 # 12:00
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afternoon:
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earliest: 780 # 13:00
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latest: 960 # 16:00
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evening:
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earliest: 1020 # 17:00
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latest: 1200 # 20:00
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num_clusters: 3
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cluster_labels: ["residential", "commercial", "office"]
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# Road network (paper §3.2)
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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 # base travel time noise
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use_complete_graph: true
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# Traffic congestion (paper §3.2, Table in §4.3)
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traffic:
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# hourly congestion multipliers (6:00-7:00, ..., 17:00-18:00)
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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: 1.0 # θ in Eq.9
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risk_aversion_beta: 0.3 # β in Eq.10
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uncertainty_base: 0.05 # base uncertainty proportion of travel time
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# Cost function weights (paper Eq.1)
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cost:
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lambda_lateness: 1.0 # λ₁
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lambda_congestion: 1.0 # λ₂
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lambda_stability: 0.3 # λ₃ (for RRD only)
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# ALNS parameters (paper §3.3.1)
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alns:
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max_iterations: 1000
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time_limit_sec: 600
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destroy_ratio_min: 0.1 # α_min
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destroy_ratio_max: 0.4 # α_max
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initial_temperature_factor: 0.05 # T₀ = factor × Z(S⁰)
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cooling_rate: 0.99975 # γ in Eq.21
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reaction_factor: 0.1 # ξ in Eq.19
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segment_length: 100 # π iterations for weight update
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stall_limit: 200 # T_stall consecutive no-improvement
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max_attempts: 5 # max attempts per candidate generation
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# Reward levels (σ₁, σ₂, σ₃, σ₄)
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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 (paper §3.3.2)
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tabu:
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move_tabu:
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tenure: 7 # τ_move initial
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tenure_min: 3
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tenure_max: 12
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overlap_threshold: 0.5 # μ in Eq.23
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stall_for_increase: 50 # τ_stall for adaptive tenure
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solution_tabu:
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tenure: 15 # τ_sol
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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 # κ in Eq.33
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normalization_interval: 50 # ν in Eq.33
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diversification:
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delta_max: 0.7 # δ_max in Eq.27
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eta_balance: 0.5 # η in Eq.28
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weights: [0.4, 0.3, 0.3] # ω₁, ω₂, ω₃ in Eq.27
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aspiration:
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beta_threshold: 0.3 # β in Eq.30
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gamma_threshold: 0.8 # γ in Eq.31
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# RRD parameters (paper §3.3.3)
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rrd:
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rollout:
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horizon_min_min: 30 # H_min
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horizon_max_min: 120 # H_max
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urgency_alpha: 1.0 # α_urgency
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n_sim_min: 2 # N_min
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n_sim_max: 50 # N_max
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mc_iterations: 50 # Monte Carlo iterations
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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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# Weights for composite score (Eq.40)
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weight_rollout: 0.4 # ω₁
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weight_stability: 0.3 # ω₂
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weight_recovery: 0.3 # ω₃
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tabu:
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penalty: 50.0 # τ_penalty in Eq.39
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bonus: 25.0 # τ_bonus in Eq.39
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events:
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urgency_threshold: 0.5 # trigger threshold
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# Event weights (α_e, β_e, γ_e in Eq.35)
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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 # |A_e| ≤ 20
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# Experiment settings (paper §4.8, §5)
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experiments:
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random_seeds: 30 # 30 runs per configuration
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seed_start: 1 # seed range: 1..30
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report_mean_std: true
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# Sensitivity analysis ranges (paper §5.1)
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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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# Uncertainty levels (paper §5.2)
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robustness:
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sigma_values: [0.1, 0.2, 0.3, 0.5]
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