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)
117 lines
4.8 KiB
Markdown
117 lines
4.8 KiB
Markdown
# T-ALNS-RRD Reproduction
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Reproduction of the paper **"Optimizing urban last mile delivery efficiency through dynamic vehicle routing heuristics and traffic flow analysis"** (Liu & Wang, 2025).
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## Overview
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This project reproduces the core algorithmic framework of T-ALNS-RRD using a **custom synthetic dataset** (methodological reproduction). The reproduction validates the following trends:
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1. Traffic-aware cost reduces congestion exposure
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2. ALNS provides further cost reduction over greedy heuristics
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3. Tabu memory improves solution stability
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4. RRD real-time dispatch handles disruption events
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## Project Structure
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```
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t_alns_rrd_reproduction/
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├── configs/default.yaml # All parameters (aligned with paper)
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├── src/
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│ ├── data_generator.py # Synthetic data generation
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│ ├── problem.py # DVRPTW-TA formulation
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│ ├── cost.py # Cost functions (Eq.1, Eq.16-17)
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│ ├── baselines/
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│ │ ├── static_vrptw.py # Static VRPTW greedy
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│ │ └── ta_greedy.py # Traffic-aware greedy
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│ ├── alns/
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│ │ ├── operators_destroy.py # Destroy operators (random/worst/related)
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│ │ ├── operators_repair.py # Repair operators (greedy/regret2/time-window)
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│ │ ├── acceptance.py # Simulated annealing
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│ │ └── alns_base.py # ALNS-Base (Algorithm 1)
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│ ├── tabu/
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│ │ ├── move_tabu.py # Move-based Tabu (Eq.22-23)
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│ │ ├── solution_tabu.py # Solution hash memory (Eq.24)
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│ │ ├── frequency_memory.py # Frequency matrices (Eq.25-26)
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│ │ └── t_alns.py # T-ALNS (Algorithm 2)
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│ ├── rrd/
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│ │ ├── event_generator.py # Event detection (Eq.35)
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│ │ ├── candidate_actions.py # Action generation
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│ │ ├── rollout.py # Rollout simulation (Eq.36-39)
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│ │ ├── dispatch.py # Dispatch decision (Eq.40-42)
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│ │ └── t_alns_rrd.py # T-ALNS-RRD (Algorithm 3)
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│ ├── experiments/
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│ │ └── run_main_comparison.py
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│ └── visualization/
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│ └── plot_results.py
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├── data/synthetic/ # Generated datasets
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├── results/ # Experiment outputs
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└── requirements.txt
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```
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## Quick Start
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```bash
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# Install dependencies
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pip install -r requirements.txt
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# Generate data and run quick test
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python3 -c "
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from src.data_generator import DataGenerator
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from src.problem import ProblemContext
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from src.cost import CostCalculator
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from src.alns.alns_base import ALNSBase
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from src.tabu.t_alns import TALNS
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data = DataGenerator(seed=42).generate_all()
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ctx = ProblemContext(
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customers={c.customer_id: c for c in data['customers']},
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depot=data['depot'], traffic=data['traffic'],
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n_vehicles=data['n_vehicles'], vehicle_capacity=data['vehicle_capacity'],
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)
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cc = CostCalculator()
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alns = ALNSBase(ctx, cc, config={'max_iterations': 100, 'time_limit_sec': 60})
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result = alns.run(seed=42)
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print(f'ALNS: Cost={result[\"total_cost\"]:.1f} OTDR={result[\"otdr\"]*100:.1f}%')
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talns = TALNS(ctx, cc, config={'max_iterations': 100, 'time_limit_sec': 60})
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result2 = talns.run(seed=42)
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print(f'T-ALNS: Cost={result2[\"total_cost\"]:.1f} OTDR={result2[\"otdr\"]*100:.1f}%')
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"
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# Run full comparison experiment
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python3 src/experiments/run_main_comparison.py
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```
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## Key Algorithms
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| Algorithm | Description | Paper Reference |
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|-----------|-------------|-----------------|
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| Static-VRPTW | Greedy with static travel times | Baseline 1 |
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| TA-VRPTW-Greedy | Greedy with time-dependent costs | Baseline 2 |
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| ALNS-Base | Adaptive destroy-repair with SA | Algorithm 1 |
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| T-ALNS | ALNS + 3-layer Tabu memory | Algorithm 2 |
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| T-ALNS-RRD | T-ALNS + rollout-based dispatch | Algorithm 3 |
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## Experiment Configurations
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- Dataset: 47 customers, 1 depot, 4 vehicles (120kg capacity)
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- Traffic: 12 one-hour intervals, 6:00-18:00
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- Area: 8 × 10 km² urban zone
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- Complete graph: 2,256 arcs, 81,200+ traffic data points
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## Expected Trends
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Results should show progressive improvement:
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```
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Static-VRPTW (highest cost, lowest OTDR)
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→ TA-Greedy (reduced congestion)
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→ ALNS-Base (further cost reduction)
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→ T-ALNS (better stability)
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→ T-ALNS-RRD (best under disruptions)
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```
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## Reproduction Statement
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> Since the original dataset requires reasonable request to the authors and is not yet publicly available, this project constructs a custom synthetic dataset matching the experimental scale and structure described in the paper to reproduce its core algorithmic framework and comparative experimental logic. This is a **methodological reproduction** focused on verifying the relative impact of different algorithmic modules, not an exact numerical replication.
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