An intelligent multi-agent system for last-mile logistics

Masoud Kahalimoghadam, Russell G. Thompson and Abbas Rajabifard

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 200, issue C

Abstract: Operational efficiency in last-mile logistics (LML) is often hindered by fluctuating e-commerce demand, unforeseen disruptions, and diverse stakeholders with evolving objectives. This paper aims to evaluate the effectiveness of Physical Internet hubs (PI-hubs) in addressing LML challenges by developing an intelligent multi-agent system (iMAS) that focuses on stakeholders’ interactions. In the iMAS, carriers, shippers, and Physical Internet managers (PI-Managers) are considered learning agents. In this complex scenario, the distribution network (DN) structure is dynamic, transitioning from a single-tier system to a two-tier network when carriers and shippers utilize PI-hubs. Bayesian Q-learning optimizes action selection by balancing exploration and exploitation, while fair reward distribution aligns agent incentives, improving cooperation, stability, and performance in dynamic, multi-agent environments. Simulations involving varying combinations of learning agents are performed. Two delivery vehicle types are also included in the collaborative vehicle routing problem, forming the iMAS environment. The simulation results are compared with the base case where agents do not engage in learning. Findings suggest that when PI-managers engage in learning, there is an increase in the percentage of PI-hub usage and a decrease in total vehicle kilometers traveled (VKT), highlighting the effectiveness of PI-hubs in alleviating the adverse impacts of freight vehicle mobility within metropolitan areas. The impact of the initial PI-hub fee policy on DN efficiency, including PI-hub usage, VKT, carriers’ and shippers’ costs, and PI-Manager profit, is assessed through extensive sensitivity analysis. The iMAS acts as a decision support system enabling policymakers to evaluate various policies and actions, aiding the identification of optimal decisions within the LML framework.

Keywords: Multi-agent system; Last-mile delivery; Bayesian reinforcement learning; Q-learning; Nash social welfare; Distribution network design (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104191

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