•  
  •  
 

Keywords

Hierarchical control Semi, heterarchical control Flexible manufacturing system (FMS) Demand volume Demand variety

Document Type

Research Paper

Abstract

Current expectations demand that manufacturing control systems exhibit enhanced flexibility and agility. Concurrently, using a multi-agent manufacturing system has been regarded as a crucial strategy for addressing the challenges associated with dynamics and unpredictability within the setting of part processing. This paper presents a novel switching mechanism for a hybrid control multi-agent system. The proposed hybrid control model combines the benefits of semi-heterarchical and hierarchical structures, enabling the successful implementation of adaptive control strategies. The aim is to enhance the implementation of a multi-agent control system in a dynamic manufacturing environment. This study checks how well the suggested switching mechanism in a hybrid control multi-agent system by examining its performance across many metrics, including processing time, throughput, cycle time, and utilization of resources. The results show that a semi-heterarchical control architecture system has superior outcomes to a hierarchical control structure. The evaluation of a production control policy typically necessitates the utilization of simulation modeling, as it involves complex interactions. In this regard, the Matlab 2022/Simulink software package was employed. This study was conducted in response to the limited number of comprehensive studies that have described the implementation of this particular program.

References

Vespoli, G. Guizzi, E. Gebennini and A. Grassi, A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture, Ann. Oper. Res., 310 (2022) 201-221.‏ https://doi.org/10.1007/s10479-021-04184-z A. Dittrich and S. Fohlmeister, Cooperative multi-agent system for production control using reinforcement learning, CIRP Annals, 69 (2020) 389-392.‏ Roa, J. F. Jimenez and G. Zambrano-Rey, Directive mode for the semi-heterarchical control architecture of a flexible manufacturing system, IFAC-Papers On Line, 52 (2019) 19-24.‏ https://doi.org/10.1016/j.ifacol.2019.10.013 Kuhnle, J. P. Kaiser, F. Theiß, N. Stricker, and G. Lanza, Designing an adaptive production control system using reinforcement learning, J. Intell. Manuf., 32 (2021) 855-876.‏ https://link.springer.com/article/10.1007/s10845-020-01612-y R. Pfeifer, Operative Production Controlling as Entrance into Controlling 4.0., Trends Econ. Manage.,  15 (2021).‏ https://doi.org/10.13164/trends.2021.37.73 Mayer, C. Arnet, D. Gankin and C. Endisch, Standardized framework for evaluating centralized and decentralized control systems in modular assembly systems, In 2019 IEEE Int. Conf. on systems, man and cybernetics (SMC),  113-119, 2019. https://doi.org/10.1109/SMC.2019.8914314 R. Boccella, P. Centobelli, R .Cerchione, T. Murino and R. Riedel, Evaluating centralized and heterarchical control of smart manufacturing systems in the era of Industry 4.0, Appl. Sci., 10 (2020) 755.‏ https://doi.org/10.3390/app10030755 Dassisti, A. Giovannini, P. Merla, M. Chimienti, and H. Panetto, Hybrid production-system control-architecture for smart manufacturing, In on the Move to Meaningful Internet Systems. OTM 2017 Workshops: Confederated International Workshops, EI2N, FBM, ICSP, Meta4eS, OTMA 2017 and ODBASE Posters 2017, Rhodes, Greece, October 23–28, 2017, Revised Selected Papers, (2017) 5-15. ‏https://dx.doi.org/10.1007/978-3-319-73805-5_1 Pach, T. Berger, T. Bonte and D. Trentesaux, ORCA-FMS: A dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling, Comput. Ind., 65 (2014) 706-720.‏ https://doi.org/10.1016/j.compind.2014.02.005 F. Jimenez, A. Bekrar, D.Trentesaux and P. Leitão, A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures, Int. J. Prod. Res., 54 (2016) 7027-7042.‏ https://doi.org/10.1080/00207543.2016.1177237 Meissner, R. Ilsen and J. C. Aurich, Analysis of control architectures in the context of Industry 4.0., Procedia cirp, 62 (2017) 165-169.‏ https://doi.org/10.1016/j.procir.2016.06.113 Grassi, G.Guizzi, L. C.Santillo and S. Vespoli, A semi-heterarchical production control architecture for industry 4.0-based manufacturing systems, Manuf. Lett., 24 (2020) 43-46.‏ https://doi.org/10.1016/j.mfglet.2020.03.007 K.Ismayyir, L. M.Dawood and M.AL-Khafaji, Modelling and control architectures of production systems: Literature review, AIP Conf. Proc., 3079, 2024,060022. https://doi.org/10.1063/5.0202238 Ebufegha A. J, Decentralized Scheduling Using the Multi-Agent System Approach for Smart Manufacturing Systems: Investigation and Design. Ph.D. thesis, University of Calgary, Canada, 2023. Salatiello, S. Vespoli, G. Guizzi and A. Grassi, Long-sighted dispatching rules for manufacturing scheduling problem in i4. 0 decentralized approach, Comput. Ind. Eng., 190 ‏(2023) 110006. https://doi.org/10.1016/j.cie.2024.110006 L. L.Wynn, T. Boonraksa, P. Boonraksa, W. Pinthurat and B. Marungsri, Decentralized energy management system in microgrid considering uncertainty and demand response, Electronics, 12 (2023) 237. https://doi.org/10.3390/electronics12010237 Zhao, H. Wang, B. Niu, X. Zhao and N. Xu, Adaptive fuzzy decentralized optimal control for interconnected nonlinear systems with unmodeled dynamics via mixed data and event driven method, Fuzzy Sets Syst., 474 (2024) 108735. ‏https://doi.org/10.1016/j.fss.2023.108735 Chen, K. Zhang, Y. Wang, X. Yin, Z. Li and D. Filev, Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control, IEEE Trans. Intell. Veh., (2024) 1-14. https://doi.org/10.48550/arXiv.2308.02345 K. Ismayir, L. M.Dawood and M. M. AL-Khafaji, Performance Evaluation of a Production Control Architectures for Flexible Manufacturing System, Adv. Sci. Technol, Res. J., 18 (2024) 175-187. https://doi.org/10.12913/22998624/186222 C. Gong, A decision-making perspective on hybrid manufacturing system control, Int. J. Prod. Res., 35 (1997) 1945-1960.‏ https://doi.org/10.1080/002075497195001 Ma, A. Nassehi, and C. Snider, Anarchic manufacturing: implementing fully distributed control and planning in assembl, Manuf. Prod. Res., 9 (2021) 56-80.‏ https://doi.org/10.1080/21693277.2021.1963346 J. Crowe and E. J. Stahlman, A proposed structure for distributed shopfloor control, Integr. Manuf. Syst., 6 (1995) 31-36.‏ https://doi.org/10.1108/09576069510099356 F. Babiceanu, F. F. Chen and R. H. Sturges, Framework for the control of automated material-handling systems using the holonic manufacturing approach, Int. J. Prod. Res., 42 (2004) 3551-3564.‏ https://doi.org/10.1080/00207540410001705284 M. Lima, R. M. Sousa and P. J. Martins, Distributed production planning and control agent-based system, Int. J. Prod. Res., 44 (2006) 3693-3709.‏ https://doi.org/10.1080/00207540600788992 Zwegers, A. J. R., Pels, H. J., Schrijver, R. L. J. and van den Berg, R. J., An agent based control system for a model factory, Advances in Production Management Systems: Perspectives and future challenges, 103-114, 1998. X. Dou and B. Liu, Multi-agent based hierarchical hybrid control for smart microgrid, IEEE Trans. Smart Grid, 4 (2013) 771-778. https://doi.org/10.1109/TSG.2012.2230197 Shen and D. H. Norrie, Agent-based systems for intelligent manufacturing: a state-of-the-art survey, Knowl. Inf. Syst., 1 (1999) 129-156.‏ https://doi.org/10.1007/BF03325096 Trentesaux, C. Pach, A. Bekrar, Y. Sallez, T. Berger, T.Bonte and J.Barbosa, Benchmarking flexible job-shop scheduling and control systems, Control Eng. Pract., 21 (2013) 1204-1225. ‏https://doi.org/10.1016/j.conengprac.2013.05.004

Highlights

The hybrid model melds semi-heterarchical and hierarchical benefits, enabling adaptive control. Switching to semi-heterarchical structures can boost efficiency, especially with operational deviations. Implementing semi-heterarchical control yields the most optimal production outcomes.

DOI

10.30684/etj.2024.148242.1725

First Page

794

Last Page

807

Download

Share

COinS