Implementation of an Agent System to Increase Manufacturing Process Efficiency in a Smart Factory
Abstract
The rapid advancement of Industry 4.0 technologies has transformed traditional manufacturing into highly interconnected smart factory systems. However, achieving optimal efficiency in such environments remains challenging due to complex production flows and the need for real-time decision-making. This study explores the implementation of an agent-based system to improve efficiency within a smart factory setting, focusing on how autonomous agents can manage, coordinate, and optimize manufacturing processes. The research aims to analyze the effectiveness of agent systems in reducing production delays, enhancing resource allocation, and improving overall productivity. A combination of simulation and experimental analysis was employed to assess the impact of agent-based solutions on production efficiency. The agent system was integrated into the smart factory model, where agents performed tasks such as process monitoring, predictive maintenance scheduling, and dynamic resource management. Results indicate that the agent system contributed to a 15% reduction in idle time, a 20% improvement in machine utilization, and an overall increase in production throughput. These improvements highlight the potential of agent systems to address inefficiencies in manufacturing by enabling adaptive and autonomous decision-making processes. The findings suggest that agent-based systems are viable solutions for enhancing operational efficiency in smart factories, paving the way for further innovations in automated manufacturing environments. Implementing such systems could lead to more resilient, responsive, and efficient manufacturing processes, ultimately supporting the broader adoption of smart factory practices in the industry.
Full text article
References
Ajidarma, P., & Nof, S. Y. (2024). Human-Robot Collaborative Reinforcement Learning in Semi-Automated Manufacturing Operations. Dalam Ansari F. & Schlund S. (Ed.), IFAC-PapersOnLine (Vol. 58, Nomor 19, hlm. 528–532). Elsevier B.V.; Scopus. https://doi.org/10.1016/j.ifacol.2024.09.266
Bharathy, P., & Thanikachalam, P. V. (2024). Recent Advances and Future Prospects in Polymer-Mediated Drug Delivery Systems: A Comprehensive Review. International Journal of Drug Delivery Technology, 14(3), 1896–1907. Scopus. https://doi.org/10.25258/ijddt.14.3.89
Bozzi, A., Jimenez, J.-F., Hernandez-Rodriguez, C., Gonzalez-Neira, E.-M., & Trentesaux, D. (2023). Platoon-Based Distributed Control for Automated Material Handling Systems. Int. Conf. Control, Decis. Inf. Technol., CoDIT, 2257–2262. Scopus. https://doi.org/10.1109/CoDIT58514.2023.10284111
Cardillo Albarrán, J., Chacón Ramírez, E., Cruz Salazar, L. A., & Paredes Astudillo, Y. A. (2021). Digital Twin in Water Supply Systems to Industry 4.0: The Holonic Production Unit. Dalam Trentesaux D., Borangiu T., Leitão P., Jimenez J., & Montoya-Torres J.R. (Ed.), Stud. Comput. Intell. (Vol. 987, hlm. 42–54). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-030-80906-5_4
Cavata, J. T., Massote, A. A., Maia, R. F., & Lima, F. (2020). Highlighting the benefits of industry 4.0 for production: An agent-based simulation approach. Gestao e Producao, 27(3). Scopus. https://doi.org/10.1590/0104-530x5619-20
Chemweno, P., Sullivan, B. P., Bermperidis, G., & Thiede, S. (2022). Exploring the Added-Value of Integrating Real-Time Location Systems for Tracking Critical Maintenance Tools. Dalam Valente A., Carpanzano E., & Boer C. (Ed.), Procedia CIRP (Vol. 107, hlm. 902–907). Elsevier B.V.; Scopus. https://doi.org/10.1016/j.procir.2022.05.082
Chouikhi, S., Esseghir, M., & Merghem-Boulahia, L. (2024). Energy-Efficient Computation Offloading Based on Multiagent Deep Reinforcement Learning for Industrial Internet of Things Systems. IEEE Internet of Things Journal, 11(7), 12228–12239. Scopus. https://doi.org/10.1109/JIOT.2023.3333044
Concli F., Maccioni L., Vidoni R., & Matt D.T. (Ed.). (2024). 3rd International Symposium on Industrial Engineering and Automation, ISIEA 2024. Lecture Notes in Networks and Systems, 1124 LNNS. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207856692&partnerID=40&md5=88e73c4b0b8e78c6239544ab3167799b
El-Haouzi, H. B., & Valette, E. (2021). Human system integration as a key approach to design manufacturing control system for industry 4.0: Challenges, barriers, and opportunities. IFAC-PapersOnLine, 54(1), 263–268. Scopus. https://doi.org/10.1016/j.ifacol.2021.08.031
Gorodetsky, V. I., Kozhevnikov, S. S., Novichkov, D., & Skobelev, P. O. (2019). The Framework for Designing Autonomous Cyber-Physical Multi-agent Systems for Adaptive Resource Management. Dalam Marík V., Kadera P., Rzevski G., Zoitl A., Anderst-Kotsis G., Khalil I., & Tjoa A.M. (Ed.), Lect. Notes Comput. Sci.: Vol. 11710 LNAI (hlm. 52–64). Springer; Scopus. https://doi.org/10.1007/978-3-030-27878-6_5
Halaška, M., & Šperka, R. (2019). Performance of an automated process model discovery—The logistics process of a manufacturing company. Engineering Management in Production and Services, 11(2), 106–118. Scopus. https://doi.org/10.2478/emj-2019-0014
Hartikainen, M., Spurava, G., & Väänänen, K. (2024). Human-AI Collaboration in Smart Manufacturing: Key Concepts and Framework for Design. Dalam Lorig F., Tucker J., Lindstrom A.D., Dignum F., Murukannaiah P., Theodorou A., & Yolum P. (Ed.), Front. Artif. Intell. Appl. (Vol. 386, hlm. 162–172). IOS Press BV; Scopus. https://doi.org/10.3233/FAIA240192
Heik, D., Bahrpeyma, F., & Reichelt, D. (2024). Study on the application of single-agent and multi-agent reinforcement learning to dynamic scheduling in manufacturing environments with growing complexity: Case study on the synthesis of an industrial IoT Test Bed. Journal of Manufacturing Systems, 77, 525–557. Scopus. https://doi.org/10.1016/j.jmsy.2024.09.019
Hu, H., Jia, X., Liu, K., & Sun, B. (2021). Self-Adaptive Traffic Control Model With Behavior Trees and Reinforcement Learning for AGV in Industry 4.0. IEEE Transactions on Industrial Informatics, 17(12), 7968–7979. Scopus. https://doi.org/10.1109/TII.2021.3059676
Ilin I., Kudryavtseva T., & Petrova M.M. (Ed.). (2023). International Scientific Conference on Digital Transformation on Manufacturing, Infrastructure and Service, DTMIS 2022. Lecture Notes in Networks and Systems, 684 LNNS. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169002872&partnerID=40&md5=5ee1eb263ec234811c6ee90190b95e64
Imran, M., Antonucci, G., Di Giorgio, A., Priscoli, F. D., Tortorelli, A., & Liberati, F. (2023). Task Scheduling in Assembly Lines with Single-Agent Deep Reinforcement Learning. Int. Conf. Control, Decis. Inf. Technol., CoDIT, 1583–1588. Scopus. https://doi.org/10.1109/CoDIT58514.2023.10284428
Jankovi?, D., Šimic, M., & Herakovi?, N. (2021). The Concept of Smart Hydraulic Press. Dalam Borangiu T., Trentesaux D., Leitão P., Cardin O., & Lamouri S. (Ed.), Stud. Comput. Intell. (Vol. 952, hlm. 409–420). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-030-69373-2_29
Kalyani, Y., & Collier, R. (2023). Hypermedia Multi-Agents, Semantic Web, and Microservices to Enhance Smart Agriculture Digital Twin?. IEEE Int. Conf. Pervasive Comput. Commun. Workshops Other Affil. Events, PerCom Workshops, 170–171. Scopus. https://doi.org/10.1109/PerComWorkshops56833.2023.10150413
Kim K., Rickli J., & Monplaisir L. (Ed.). (2023). 31st International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022. Lecture Notes in Mechanical Engineering. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148236036&partnerID=40&md5=e0f0ed4a0aabdf38638afce21493e073
Koposov, A. S., & Pakshin, P. V. (2023). Iterative Learning Control of Stochastic Multi-Agent Systems with Variable Reference Trajectory and Topology. Automation and Remote Control, 84(6), 612–625. Scopus. https://doi.org/10.1134/S0005117923060073
Li, H., & Qin, S. (2024). A Neurodynamic Approach for Solving Time-Dependent Nonlinear Equation System: A Distributed Optimization Perspective. IEEE Transactions on Industrial Informatics, 20(8), 10031–10039. Scopus. https://doi.org/10.1109/TII.2024.3383508
Li, Z., Zhong, R. Y., Tian, Z. G., Dai, H.-N., Barenji, A. V., & Huang, G. Q. (2021). Industrial Blockchain: A state-of-the-art Survey. Robotics and Computer-Integrated Manufacturing, 70. Scopus. https://doi.org/10.1016/j.rcim.2021.102124
Liu, F., Huang, Y., & Li, Z. (2024). Construction and Research of Intelligent Manufacturing System Components Based on Agent Concept. Int. Conf. Mechatronics Technol. Intell. Manuf., ICMTIM, 10–14. Scopus. https://doi.org/10.1109/ICMTIM62047.2024.10629256
Luan, C., Yao, X., Zhang, C., Fu, J., & Wang, B. (2020). Integrated self-monitoring and self-healing continuous carbon fiber reinforced thermoplastic structures using dual-material three-dimensional printing technology. Composites Science and Technology, 188. Scopus. https://doi.org/10.1016/j.compscitech.2019.107986
Maloney, M., Reilly, E., Siegel, M., & Falco, G. (2019). Cyber physical iot device management using a lightweight agent. Proc. - IEEE Int. Congr. Cybermatics: IEEE Int. Conf. Internet Things, IEEE Int. Conf. Green Comput. Commun., IEEE Int. Conf. Cyber, Phys. Soc. Comput. IEEE Int. Conf. Smart Data, iThings/GreenCom/CPSCom/SmartData, 1009–1014. Scopus. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00176
Nie, Z., & Chen, K.-C. (2022). Hypergraphical Real-Time Multirobot Task Allocation in a Smart Factory. IEEE Transactions on Industrial Informatics, 18(9), 6047–6056. Scopus. https://doi.org/10.1109/TII.2021.3135297
Nouiri, M., Trentesaux, D., & Bekrar, A. (2019). Towards energy efficient scheduling of manufacturing systems through collaboration between cyber physical production and energy systems. Energies, 12(23). Scopus. https://doi.org/10.3390/en12234448
Nouiri, M., Trentesaux, D., Bekrar, A., Giret, A., & Salido, M. A. (2019). Cooperation between smart manufacturing scheduling systems and energy providers: A multi-agent perspective. Dalam Cavalieri S., Thomas A., Trentesaux D., & Borangiu T. (Ed.), Stud. Comput. Intell. (Vol. 803, hlm. 197–210). Springer Verlag; Scopus. https://doi.org/10.1007/978-3-030-03003-2_15
Phasinam, K., Usman, M., Bhattacharya, S., Kassanuk, T., & Tongkachok, K. (2022). Comparative Analysis of Environmental Internet of Things (IoT) and Its Techniques to Improve Profit Margin in a Small Business. Dalam Balas V.E., Sinha G.R., Agarwal B., Sharma T.K., Dadheech P., & Mahrishi M. (Ed.), Commun. Comput. Info. Sci.: Vol. 1591 CCIS (hlm. 160–168). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-07012-9_14
Ran, P., Jiang, B., Wang, S., Li, X., & Qin, L. (2024). Dynamic Hybrid Flow Shop Scheduling in Multi-Agent Manufacturing Systems via Federated Transfer Learning. Dalam Na J. & Sun J. (Ed.), Chinese Control Conf., CCC (hlm. 6893–6898). IEEE Computer Society; Scopus. https://doi.org/10.23919/CCC63176.2024.10662114
Reffad, H., & Alti, A. (2023). Semantic-Based Multi-Objective Optimization for QoS and Energy Efficiency in IoT, Fog, and Cloud ERP Using Dynamic Cooperative NSGA-II. Applied Sciences (Switzerland), 13(8). Scopus. https://doi.org/10.3390/app13085218
Rocha, A. D., Arvana, M., Freitas, N., Dinis, R. M., Gouveia, T., MacHado, D., & Barata, J. (2024). Human-Centric Digital Twin-Driven Approach for Plug-and- Produce in Modular Cyber-Physical Production Systems. Dalam Facchinetti T., Cenedese A., Bello L.L., Vitturi S., Sauter T., & Tramarin F. (Ed.), IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA. Institute of Electrical and Electronics Engineers Inc.; Scopus. https://doi.org/10.1109/ETFA61755.2024.10710730
Salopek ?ubri? I., ?ubri? G., Jambroši? K., Jur?evi? Luli? T., & Sumpor D. (Ed.). (2023). Proceedings of the 9th International Ergonomics Conference, ERGONOMICS 2022. Lecture Notes in Networks and Systems, 701 LNNS. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172238841&partnerID=40&md5=c9e93b5da0cfae28df310d877efb519e
Santos, A. J., Martin, N., Outón, J., Blanco, E., García, R., & Morales, F. M. (2023). A simple two-step approach to the fabrication of VO2-based coatings with unique thermochromic features for energy-efficient smart glazing. Energy and Buildings, 285. Scopus. https://doi.org/10.1016/j.enbuild.2023.112892
Schwung, D., Reimann, J. N., Schwung, A., & Ding, S. X. (2020). Smart Manufacturing Systems: A Game Theory based Approach. Dalam Stud. Comput. Intell. (Vol. 864, hlm. 51–69). Springer; Scopus. https://doi.org/10.1007/978-3-030-38704-4_3
Semenov, A. S. (2024). Hyperagent Smart Factories Based on Fractal Petri Nets: Ensuring Elasticity and Sustainability. Int. Conf. Control, Decis. Inf. Technol., CoDIT, 425–430. Scopus. https://doi.org/10.1109/CoDIT62066.2024.10708431
Sergeyeva, T., Bronin, S., & Glazar, T. (2023). Technology for Synergistic Solutions Co-Creation Based on Multi-Agents’ Diversities Interaction. Dalam Anisimov A., Snytyuk V., Chris A., Pester A., Mallet F., Tanaka H., Krak I., Henke K., Chertov O., Marchenko O., Bozoki S., Tsyganok V., & Vovk V. (Ed.), CEUR Workshop Proc. (Vol. 3624, hlm. 462–470). CEUR-WS; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184140284&partnerID=40&md5=88b86f6d0bb01b5d3dc78fe28ecf05f8
Shu, T., Pan, Z., Ding, Z., & Zu, Z. (2024). Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm. Expert Systems with Applications, 255. Scopus. https://doi.org/10.1016/j.eswa.2024.124765
Strzelczak, S., & Marciniak, S. (2019). Architecture for production internet. Dalam Cavalieri S., Thomas A., Trentesaux D., & Borangiu T. (Ed.), Stud. Comput. Intell. (Vol. 803, hlm. 67–85). Springer Verlag; Scopus. https://doi.org/10.1007/978-3-030-03003-2_5
Sun, M., Liu, M., Zhang, X., Ling, L., Ge, M., Liu, C., & Rui, Z. (2024). Real-time rescheduling for smart shop floors: An integrated method. Flexible Services and Manufacturing Journal. Scopus. https://doi.org/10.1007/s10696-024-09574-6
Taboun, M. S., & Brennan, R. W. (2019). Reconfiguration protocols for embedded agents in wireless control networks. Dalam Ryan A., Gordon S., & Tiernan P. (Ed.), Procedia Manuf. (Vol. 38, hlm. 589–596). Elsevier B.V.; Scopus. https://doi.org/10.1016/j.promfg.2020.01.074
Tao, Y., Guo, Y., Pan, Y., Huang, S., Qian, W., & Xie, J. (2024). Digital twin-driven cloud manufacturing system: An implementation framework, operating mechanism and key technologies. International Journal of Computer Integrated Manufacturing. Scopus. https://doi.org/10.1080/0951192X.2024.2428691
Ud Din, F., & Paul, D. (2023). Demystifying xAOSF/AOSR Framework in the Context of Digital Twin and Industry 4.0. Dalam Arai K. (Ed.), Lect. Notes Networks Syst.: Vol. 544 LNNS (hlm. 600–610). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-3-031-16075-2_44
Uslu, B. Ç. (2023). The role of MAS interoperability for IoT applications: A survey on recent advances in manufacturing systems. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(2), 1279–1297. Scopus. https://doi.org/10.17341/gazimmfd.944264
Vermesan, O., John, R., de Luca, C., & Coppola, M. (2021). Artificial intelligence for digitising industry: Applications. Dalam Artif. Intell. For digit. Ind.: Appl. (hlm. 396). River Publishers; Scopus. https://doi.org/10.13052/rp-9788770226639
Wan, J., Li, X., Dai, H.-N., Kusiak, A., Martinez-Garcia, M., & Li, D. (2021). Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges. Proceedings of the IEEE, 109(4), 377–398. Scopus. https://doi.org/10.1109/JPROC.2020.3034808
Wang, K.-J., & Eunike, A. (2024). Negotiation-based scheduling considering agent emotion. Expert Systems with Applications, 255. Scopus. https://doi.org/10.1016/j.eswa.2024.124905
Xing, J., Ma, Y., Cai, J., Shi, J., & Liu, J. (2023). Distributed Scheduling Method for Smart Shop Floor Based on QMIX. IEEE Int. Conf. Autom. Sci. Eng., 2023-August. Scopus. https://doi.org/10.1109/CASE56687.2023.10260396
Zakhama, A., Charrabi, L., & Jelassi, K. (2019). Intelligent Selective Compliance Articulated Robot Arm robot with object recognition in a multi-agent manufacturing system. International Journal of Advanced Robotic Systems, 16(2). Scopus. https://doi.org/10.1177/1729881419841145
Zhou, T., Tang, D., Zhu, H., & Zhang, Z. (2021). Multi-agent reinforcement learning for online scheduling in smart factories. Robotics and Computer-Integrated Manufacturing, 72. Scopus. https://doi.org/10.1016/j.rcim.2021.102202
Authors
Copyright (c) 2024 Budi Nugroho, Hiras Pasaribu, Schersclight Oscar

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.