TY - GEN
T1 - Large Language Models to Support Altruistic Collaborative Healing in Smart Manufacturing
AU - Estrada-Jimenez, Luis A.
AU - Farhadighalati, Nastaran
AU - Kalateh, Sepideh
AU - Nikghadam-Hojjati, Sanaz
AU - Barata, José
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/OE/2023.03106.BD/PT#
This research was partly supported by the Portuguese FCT program, Centre of Technology and Systems (CTS) CTS/00066 and Nova University Lisbon\u2019s PhD program of Department of Electronics and Computer Engineering.
Publisher Copyright:
© IFIP International Federation for Information Processing 2025.
PY - 2025
Y1 - 2025
N2 - The growing complexity of manufacturing systems calls for innovative approaches like biologicalisation (application of biological principles to industrial process design). In this context and building on our previous work on altruistic collaborative healing, where manufacturing elements (“Donors”) share resources with those in need (“Recipients”), this paper addresses key research challenges by integrating Large Language Models (LLMs). By enabling natural language communication between altruistic and recipient resources, LLMs enhance reasoning, coordination, and adaptability in dynamic manufacturing environments. To demonstrate this concept, we propose an LLM-driven framework for decision-making and resource allocation, validated through a peer-to-peer energy-sharing scenario involving Automated Guided Vehicles (AGVs). Using the gpt-4o-mini model in simulation, the results show improved system adaptability and 80% reliability. The paper concludes by discussing the implications, limitations, and future directions of this approach, emphasizing the potential of LLMs in smart manufacturing.
AB - The growing complexity of manufacturing systems calls for innovative approaches like biologicalisation (application of biological principles to industrial process design). In this context and building on our previous work on altruistic collaborative healing, where manufacturing elements (“Donors”) share resources with those in need (“Recipients”), this paper addresses key research challenges by integrating Large Language Models (LLMs). By enabling natural language communication between altruistic and recipient resources, LLMs enhance reasoning, coordination, and adaptability in dynamic manufacturing environments. To demonstrate this concept, we propose an LLM-driven framework for decision-making and resource allocation, validated through a peer-to-peer energy-sharing scenario involving Automated Guided Vehicles (AGVs). Using the gpt-4o-mini model in simulation, the results show improved system adaptability and 80% reliability. The paper concludes by discussing the implications, limitations, and future directions of this approach, emphasizing the potential of LLMs in smart manufacturing.
KW - Altruism
KW - Artificial Intelligence
KW - Biologicalisation
KW - Collective Behaviour
KW - Self-healing
KW - Self-organization
KW - Smart Manufacturing
UR - https://www.scopus.com/pages/publications/105010154684
U2 - 10.1007/978-3-031-97051-1_5
DO - 10.1007/978-3-031-97051-1_5
M3 - Conference contribution
AN - SCOPUS:105010154684
SN - 9783031970504
T3 - IFIP Advances in Information and Communication Technology
SP - 65
EP - 76
BT - Technological Innovation for AI-Powered Cyber-Physical Systems - 16th IFIP WG 5.5 / SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2025, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Ferrada, Filipa
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th IFIP WG 5.5 / SOCOLNET Advanced Doctoral Conference on Computing, Electrical, and Industrial Systems, DoCEIS 2025
Y2 - 2 July 2025 through 4 July 2025
ER -