Work

Thinking in transit: integrating LLMs and multi-agent systems for agile port-centred container logistics

Year: 2026

Type: article

Abstract: Transportation networks increasingly face disruptions that require rapid interpretation as well as coordinated operational response. This study develops and computationally evaluates an interdisciplinary framework integrating Large Language Models (LLMs), Multi-Agent Systems (MAS), and digital twins in port-centred multimodal container logistics. The framework separates contextual interpretation, operational validation, decentralized execution, and feedback-based adaptation. A controlled simulation compares four architectures: rule-based control, MAS-only coordination, classical machine learning (ML) with MAS, and LLM-supported interpretation with validation and MAS, across five disruption conditions and 30 matched replications. MAS coordination substantially improves delivery performance relative to rule-based control. LLM-supported interpretation achieves higher interpretation accuracy and fewer validation rejections than classical ML and reduces response time, but does not improve downstream delivery or recovery performance relative to classical ML+MAS. The findings show that improved semantic interpretation does not automatically translate into greater system-level agility when downstream coordination and fallback mechanisms compensate for interpretation errors. The study therefore positions LLM value as conditional rather than universal and identifies validation as a boundary mechanism separating probabilistic interpretation from operational execution.

Source: Array

DOI: 10.1016/j.array.2026.101213

Author: Eias Al Humdan

Institution: Rabdan Academy

Language: English

Topic: Maritime Ports and Logistics

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,680