How to model last-mile delivery using multi-agent simulation?
A Generic Modelling Framework for Last-Mile Delivery Systems
February 26, 2025
https://arxiv.org/pdf/2502.17633This paper proposes a generic framework for modeling last-mile delivery (LMD) systems using two multi-agent simulation tools: HUMAT (socio-cognitive agent behavior) and MASS-GT (freight transport logistics). The framework combines these tools to simulate complex urban delivery scenarios, accounting for both consumer choices and logistical operations. It’s designed to be adaptable to different cities and integrates various delivery methods like crowdshipping and parcel lockers.
Key points for LLM-based multi-agent systems:
- Integration of distinct agent models: The framework demonstrates the value of combining specialized agent models (socio-cognitive and logistical) for a more holistic simulation. This is relevant to LLM-based systems where different agents might have specialized roles and reasoning processes.
- Human behavior modeling: HUMAT's focus on social networks, individual motives, and decision-making is relevant to developing LLMs that understand and predict human behavior in complex scenarios.
- Scalability and adaptability: The framework is designed to be adaptable across different cities and scenarios, which is a key consideration for building scalable LLM-based multi-agent applications.
- Focus on real-world applications: The paper grounds its framework in real-world case studies of crowdshipping and parcel locker services, highlighting the practical potential of multi-agent systems in urban logistics. This practical focus aligns with the growing interest in applying LLMs to real-world problems.
- Data integration: The framework combines diverse data sources (socio-demographic data, survey data, network data) to drive the simulations, emphasizing the importance of data integration in building effective LLM-based multi-agent systems.