| Reference | Contribution Summary | Identified Gap |
| Imran & Won (2024) | Traffic-aware drone-truck delivery under dynamically changing congestion using Variable Neighborhood Descent (VND) heuristic. | No memory-guided search or rollout adaptation for uncertainty handling. |
| Chen et al. (2025) | Multi-objective Ant Colony Optimization (ACO) with adaptive pheromone control and capacity-aware repair approaches. | Lacks integration with traffic-aware constraints and adaptive memory components. |
| Agarwal et al. (2024) | Bi-criterion Steiner-TSPTW for last-mile Electronic Vehicle (EV) logistics balancing energy usage and safety. | Focused on energy/safety but lacks dynamic re-optimization or route revision mechanisms. |
| Moradi et al. (2024) | Two-echelon EV routing for parcel delivery with focus on hierarchical structure and energy limits. | No consideration of traffic flow patterns or dispatch-level adaptation in urban scenarios. |
| Nasution (2025) | Advanced High-performance Bus (AHB)-ACO for pickup and delivery under real-time dynamic constraints and adaptive re-routing. | No integration of multi-layered Tabu memory or simulation rollout for real-time control. |
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"content": "```mermaid\ngraph TD\n 0[\"Depot 0\"] -->|d₁ = 5[\"8:00, 10:00\"]| 1[\"1\"]\n 0 -->|d₂ = 8[\"9:00, 11:30\"]| 2[\"2\"]\n 0 -->|d₃ = 3[\"10:30, 12:00\"]| 3[\"3\"]\n 0 -->|d₄ = 6[\"11:00, 13:00\"]| 4[\"4\"]\n 0 -->|d₅ = 4[\"12:00, 14:00\"]| 5[\"5\"]\n 1 -->|d₁ = 5[\"8:00, 10:00\"]| 3\n 2 -->|d₂ = 8[\"9:00, 11:30\"]| 4\n 3 -->|d₃ = 3[\"10:30, 12:00\"]| 5\n 4 -->|d₄ = 6[\"11:00, 13:00\"]| 5\n 5 -->|d₅ = 4[\"12:00, 14:00\"]| 4\n style Depot fill:#FFD700,stroke:#FFA500,stroke-width:2px\n style Customer nodes fill:#66B2FF,stroke:#FFA500,stroke-width:2px\n classDef route fill:#FFA50FF,stroke:#FFA500;\n class A route from Vehicle 1 to Vehicle 2 via Route S⁰;\n class B route from Vehicle 2 to Vehicle 3 via Route S⁰;\n class C route from Vehicle 1 to Vehicle 5 via Route S⁰;\n```",
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"content": "```mermaid\ngraph TD\n subgraph_A[\"Destroy phase\"]\n A0[\"0\"] --> A2[\"2\"]\n A0 --> A3[\"3\"]\n A0 --> A4[\"4\"]\n A0 --> A5[\"5\"]\n A0 --> A1[\"1\"]\n A0 --> A2\n A2 --> B\n end\n\n subgraph_B[\"Repair phase\"]\n B0[\"0\"] --> B2[\"2\"]\n B0 --> B3[\"3\"]\n B0 --> B4[\"4\"]\n B0 --> B5[\"5\"]\n B0 --> B6[\"1\"]\n B0 --> B7[\"4\"]\n B0 --> B8[\"5\"]\n B0 --> B9[\"1\"]\n B0 --> B10[\"4\"]\n end\n\n subgraph_C[\"New Solution S'\"]\n C0[\"0\"] --> C2[\"2\"]\n C0 --> C3[\"3\"]\n C0 --> C4[\"4\"]\n C0 --> C5[\"5\"]\n C0 --> C6[\"1\"]\n C0 --> C7[\"4\"]\n C0 --> C8[\"1\"]\n C0 --> C9[\"4\"]\n C0 --> C10[\"1\"]\n C0 --> C11[\"4\"]\n C0 --> C12[\"1\"]\n C0 --> C13[\"4\"]\n C0 --> C14[\"1\"]\n C0 --> C15[\"4\"]\n C0 --> C16[\"1\"]\n C0 --> C17[\"4\"]\n C0 --> C18[\"1\"]\n C0 --> C19[\"4\"]\n C0 --> C20[\"1\"]\n C0 --> C21[\"4\"]\n C0 --> C22[\"1\"]\n C0 --> C23[\"4\"]\n C0 --> C24[\"1\"]\n C0 --> C25[\"4\"]\n C0 --> C26[\"1\"]\n C0 --> C27[\"4\"]\n C0 --> C28[\"1\"]\n C0 --> C29[\"4\"]\n C0 --> C30[\"1\"]\n C0 --> C31[\"4\"]\n C0 --> C32[\"1\"]\n C0 --> C33[\"4\"]\n C0 --> C34[\"1\"]\n C0 --> C35[\"4\"]\n C0 --> C36[\"1\"]\n C0 --> C37[\"4\"]\n C0 --> C38[\"1\"]\n C0 --> C39[\"4\"]\n C0 --> C40[\"1\"]\n C0 --> C41[\"4\"]\n C0 --> C42[\"1\"]\n C0 --> C43[\"4\"]\n C0 --> C44[\"1\"]\n C0 --> C45[\"4\"]\n C0 --> C46[\"1\"]\n C0 --> C47[\"4\"]\n C0 --> C48[\"1\"]\n C0 --> C49[\"4\"]\n C0 --> C50[\"1\"]\n C0 --> C51[\"4\"]\n C0 --> C52[\"1\"]\n C0 --> C53[\"4\"]\n C0 --> C54[\"1\"]\n C0 --> C55[\"4\"]\n C0 --> C56[\"1\"]\n C0 --> C57[\"4\"]\n C0 --> C58[\"1\"]\n C0 --> C59[\"4\"]\n C0 --> C60[\"1\"]\n C0 --> C61[\"4\"]\n C0 --> C62[\"1\"]\n C0 --> C63[\"4\"]\n C0 --> C64[\"1\"]\n C0 --> C65[\"4\"]\n C0 --> C66[\"1\"]\n C0 --> C67[\"4\"]\n C0 --> C68[\"1\"]\n C0 --> C69[\"4\"]\n C0 --> C70[\"1\"]\n C0 --> C71[\"4\"]\n C0 --> C72[\"1\"]\n C0 --> C73[\"4\"]\n C0 --> C74[\"1\"]\n C0 --> C75[\"4\"]\n C0 --> C76[\"1\"]\n C0 --> C77[\"4\"]\n C0 --> C78[\"1\"]\n C0 --> C79[\"4\"]\n C0 --> C80[\"1\"]\n C0 --> C81[\"4\"]\n C0 --> C82[\"1\"]\n C0 --> C83[\"4\"]\n C0 --> C84[\"1\"]\n C0 --> C85[\"4\"]\n C0 --> C86[\"1\"]\n C0 --> C87[\"4\"]\n C0 --> C88[\"1\"]\n C0 --> C89[\"4\"]\n C0 --> C90[\"1\"]\n C0 --> C91[\"4\"]\n C0 --> C92[\"1\"]\n C0 --> C93[\"4\"]\n C0 --> C94[\"1\"]\n C0 --> C95[\"4\"]\n style A fill:#f9f,stroke:#333\n style B fill:#f9f,stroke:#333\n style H fill:#ccf,stroke:#333\n style I fill:#cfc,stroke:#333\n style J fill:#fcc,stroke:#333\n style K fill:#fcc,stroke:#333\n style L fill:#fcc,stroke:#333\n style M fill:#fcc,stroke:#333\n style N fill:#fcc,stroke:#333\n style O fill:#fff,stroke:#333\n style P fill:#fff,stroke:#333\n style Q fill:#fff,stroke:#333\n style R fill:#fff,stroke:#333\n style S fill:#fff,stroke:#333\n style T fill:#fff,stroke:#333\n style U fill:#fff,stroke:#333\n style V fill:#fff,stroke:#333\n style W fill:#fff,stroke:#333\n style X fill:#fff,stroke:#333\n style Y fill:#fff,stroke:#333\n style Z fill:#fff,stroke:#333\n style AA fill:#fff,stroke:#333\n style AB fill:#fff,stroke:#333\n style AC fill:#fff,stroke:#333\n style AD fill:#fff,stroke:#333\n style AE fill:#fff,stroke:#333\n style AF fill:#fff,stroke:#333\n style AG fill:#fff,stroke:#333\n style AH fill:#fff,stroke:#333\n style AI fill:#fff,stroke:#333\n style AJ fill:#fff,stroke:#333\n style AK fill:#fff,stroke:#333\n style AL fill:#fff,stroke:#333\n style AM fill:#fff,stroke:#333\n style AN fill:#fff,stroke:#333\n style AO fill:#fff,stroke:#333\n style AP fill:#fff,stroke:#333\n style AQ fill:#fff,stroke:#333\n style AR fill:#fff,stroke:#333\n style AS fill:#fff,stroke:#333\n style AT fill:#fff,stroke:#333\n style AU fill:#fff,stroke:#333\n style AV fill:#fff,stroke:#333\n style AW fill:#fff,stroke:#333\n style AX fill:#fff,stroke:#333\n style AY fill:#fff,stroke:#333\n```",
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| Network Structure |
| Nodes | Customer locations + depot | 48 | Spatial coordinates (lat, lon) |
| Arcs | Feasible directed connections | 2,256 | Origin-destination pairs |
| Study Area | Shanghai urban zone coverage | 80 \\text{ km}^{2} | Jing'an and Huangpu districts |
| Customer Data |
| Delivery Requests | Customer service requirements | 47 | Individual delivery records |
| Package Demand | Weight per delivery | - | d_{i} \\in [3,12] \\text{ kg} |
| Service Duration | Time at customer location | - | s_{i} = 4 \\text{ minutes} |
| Time Windows | Service availability periods | - | 3-hour windows across the day |
| Vehicle Fleet |
| Fleet Size | Homogeneous delivery vehicles | 4 | K = \\{1,2,3,4\\} |
| Vehicle Capacity | Maximum load per vehicle | - | Q = 120 \\text{kg} |
| Operating Hours | Daily operational span | - | 10 h maximum |
| Traffic Data |
| Time Intervals | Temporal discretization | 12 | 1-hour intervals(6 AM-6 PM) |
| Travel Times | Arc traversal durations | 27,072 | t_{ij}^{(h)} entries |
| Congestion Weights | Traffic density penalties | 27,072 | \\gamma_{ij}^{(h)} values |
| Reliability Margins | Travel time uncertainties | 27,072 | \\eta_{ij}^{(h)} deviations |
| Total Dataset Volume |
| Arc-Time Combinations | Unique routing segments | 27,072 | (i,j,h) tuples |
| Traffic Parameters | Complete cost structure | 81,216 | All traffic-related entries |
| Total Data Points | Full experimental dataset | 82,347 | All components combined |
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