Work

Generative adaptive resilience: LLM-MILP modeling and strategy generation for port vessel scheduling

Year: 2026

Type: article

Abstract: Port scheduling systems face critical challenges from escalating disruptions and the limitations of traditional optimization, including rigid feasibility checks, opaque decision-making, and the absence of learning mechanisms. This paper introduces the generative adaptive resilience (GAR) framework, an integrated approach that advances port scheduling from static constraint satisfaction toward a dynamic, learning-enabled process. The GAR integrates generative AI (GAI), mathematical optimization, and evolutionary learning into a closed loop, enabling ports to respond to disruptions and proactively prepare for them. The framework bridges large language models (LLMs) with operations research through the Feasibility-Aware Constrained Decoding (FACD), converting human-interpretable actions (e.g., redirect, delay, resequencing) into mathematically feasible scheduling adjustments with a feasibility guarantee. The GAR explicitly models and exploits synergistic action pairs (e.g., jointly delaying a feeder vessel while redirecting a deep-sea liner), thereby achieving cost reductions through coordination coefficients. Empirical validation using Ningbo Port data shows that the GAR achieves faster policy convergence, reduced high-priority vessel delays, and faster solution time than full-resilience baselines, while maintaining equivalent redundancy. The framework maintains safe collaborative action rates and reduces peak terminal utilization. These results demonstrate the GAR’s concrete advantages: accelerated adaptation, quantifiable service improvements, and computational efficiency, providing ports with a measurable pathway from reactive operations to adaptive, learning-enabled resilience, contributing to risk and safety science in the AI Era.

Source: Safety Science

DOI: 10.1016/j.ssci.2026.107233

Authors: Zhi-Hua Hu, Yao-Na Li

Institution: Shanghai Maritime University

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: hybrid

Estimated APC paid: $4,360