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Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for

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This study introduces an integrated framework for Last-Mile Delivery (LMD) using AI and augmented/mixed reality (AR/MR) to boost efficiency. The system enhances coordination and information sharing across logistics operations.

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Area of Science:

  • Logistics and Supply Chain Management
  • Operations Research
  • Human-Computer Interaction

Background:

  • Last-Mile Delivery (LMD) faces escalating cost, timeliness, and sustainability challenges due to e-commerce growth and urbanization.
  • Current optimization strategies often isolate fleet management (AI) and warehouse operations (AR/MR), limiting overall efficiency.
  • Improved information visibility across logistics roles is crucial for overcoming these isolated approaches.

Purpose of the Study:

  • To propose an integrated Last-Mile Delivery (LMD) framework combining AI and AR/MR technologies.
  • To enhance operational efficiency and information visibility for logistics stakeholders.
  • To evaluate the feasibility and impact of the proposed integrated LMD system.

Main Methods:

  • Developed an LMD framework integrating AI for shipment allocation and route planning with a knowledge graph (KG) for decision support.
  • Incorporated large language model (LLM)-powered assistants and mixed reality (MR) headset interfaces for enhanced user interaction.
  • Prioritized AI customizability and real-time information sharing among stakeholders.

Main Results:

  • A system prototype was tested in a realistic logistics scenario in the Apulia region.
  • The integrated framework demonstrated potential for enhanced coordination and efficiency in LMD operations.
  • Identified key benefits and challenges associated with real-world implementation.

Conclusions:

  • The proposed integrated framework offers a viable solution for optimizing Last-Mile Delivery operations.
  • Real-time data sharing and advanced interfaces (LLM, MR) significantly improve logistics workforce coordination.
  • Further research and development are needed to address implementation challenges and maximize benefits.