A⁴PS: Agentic AI-assisted advanced planning and scheduling with large language models for smart manufacturing

Advanced Planning and Scheduling (APS) for manufacturing systems is becoming more complex against the backdrop of intelligent transformation and increasing demand for customisation. In real-world APS applications subject to multi-source dynamics, objective alterations, constraints removals/additions, algorithm upgrades are inevitable. Such structural changes of APS, requiring seamless coordination among experts such as production managers, modelling engineers, algorithm developers, are often lengthy and less flexible. This poses new challenges in cross-domain/inter-process coordination and rapid multi-disciplinary knowledge integration/reuse. This paper proposes a novel Agentic AI-Assisted APS (A⁴PS) framework, utilising Large Language Models (LLMs) and agents to assist modification/update processes of APS. Firstly, a multi-agentic AI-enabled workflow is designed following standard operating procedure of APS to facilitate the cross-domain agent coordination. Secondly, a multi-step knowledge augmentation method is proposed to endow LLM agents with specialised APS knowledge. Thirdly, a Retrieval-Augmented Generation (RAG) and Chain of Thought (CoT)-enhanced method is developed for knowledge use and interaction. Experiments are conducted with an APS dataset which is created based on classical APS cases and manufacturing researchers. Compared with basic LLMs, A⁴PS exhibited substantially superior performance across both basic and complex cases in metrics such as modelling task success rate, absolute percentage error of solution results, optimisation algorithm code logic completion rate, and code executability rate. Case study demonstrates that A⁴PS enables LLMs to coordinate, learn APS knowledge, and imitate experts in the reasoning process, and ultimately realise APS assistance using natural language. This work proposes a novel solution that uses LLMs and agentic AI to assist APS modification/update process, contributing to AI-driven smart manufacturing in Industry 4.0.