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Tytuł artykułu

Simulation Based Performance Assessment of Developing Heterarchical Control for a Flexible Assembly System

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In order to increase the robustness and flexibility of manufacturing systems under dynamic conditions which represented by disturbances that caused by uncertainties in customer demand, the heterarchical control multiagents’ system is increasingly being applied. Heterarchical model adopt the structure which has no centralized control as the agents in heterarchical model are distributed connected in a peer relation. The paper examines the developed heterarchical control multi-agents’ system in flexible assembly system, demonstrates its feasibility by simulation. The purpose of this study is to assess and enhance the efficiency, adaptability, and overall efficacy of the control system. This assessment is crucial for ensuring that the system can effectively manage dynamic and sophisticated situations. Production control model based on heterarchical structure was developed by utilize the MATLAB/SIMULINK software package, and look at the effects of changes on the manufacturing environment, specifically how changes in consumer demand in term of quantity and variety that affects the model. The process entails developing a computational model that imitates the behavior of the heterarchical control system in order to predict results. The simulation procedure included specifying the parameters and initial conditions of the system, executing the simulation, and assessing the outcomes. The results indicated a rise in throughput as well as an increase in the utilization of both the machine and material, as well as reduction in cycle time by facilitating quicker modifications to processes. The implemented approach resulted in an increase in the profitability of the manufacturing firm, meeting customer demand that making it more competitive in the marketplace.
Wydawca
Rocznik
Tom
Strony
115--122
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Production Engineering and Metallurgy Department University of Technology, Iraq
  • Production Engineering and Metallurgy Department University of Technology, Iraq
  • Production Engineering and Metallurgy Department University of Technology, Iraq
Bibliografia
  • [1] M. Dassisti, A. Giovannini, P. Merla, M. “Chimienti, 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, Springer International Publishing, pp. 5–15, 2018.
  • [2] A.F. Buckhorst, L. Grahn, R.H. Schmitt, “Decentralized holonic control system model for line-less mobile assembly systems”. Robotics and Computer-Integrated Manufacturing, 75, 102301, 2022.
  • [3] D.H. Tohma, A.K. Hamoudi, “Design of adaptive sliding mode controller for uncertain pendulum system”. Eng. Technol. J, vol.39, pp. 355–369, 2021.
  • [4] Z.T. Jabur, L.M. Dawood, “Analysis of information flow for job-shop production system”. Eng. Technol. J, vol, 33(1), pp. 223–236, 2015.
  • [5] J. Roa, J.F. Jimenez, G. Zambrano-Rey. “Directive mode for the semi-heterarchical control architecture of a flexible manufacturing system”. IFAC-Papers OnLine, vol. 52(10), pp. 19–24, 2019.
  • [6] C. Wolfsgruber. “Informatization in Production Planning and Control a Simulation based Evaluation of the Impacts in Flow Shop Production Systems”. Ph.D. thesis, University of Technology, Graz, 2016.
  • [7] A.R. Boccella, P. Centobelli, R. Cerchione, T. Murino, R. Riedel. “Evaluating centralized and heterarchical control of smart manufacturing systems in the era of Industry 4.0”. Applied Sciences, vol. 10(3), pp. 755, 2020.
  • [8] N. Zbib, C. Pach, Y. Sallez, D. Trentesaux, “Heterarchical production control in manufacturing systems using the potential fields concept”. Journal of Intelligent Manufacturing, vol. 23, pp. 1649–1670, 2012.
  • [9] J.F. Jimenez, A. Bekrar, G. Zambrano-Rey, D. Trentesaux, P. Leitão. “Pollux: a dynamic hybrid control architecture for flexible job shop systems”. International Journal of Production Research, vol. 55(15), pp. 4229–4247, 2017.
  • [10] C. Pach, T. Berger, T. Bonte, D. Trentesaux. “ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling”. Computers in Industry, vol. 65(4), pp. 706–720, 2014.
  • [11] S.R. Gonzalez, G.M. Zambrano, I.F. Mondragon. “Semi-heterarchical architecture to AGV adjustable autonomy within FMSs”. IFAC-Papers OnLine, vol. 52(10), pp. 7–12, 2019.
  • [12] D. Trentesaux, C. Pach, A. Bekrar, Y. Sallez, T. Berger, T. Bonte, J. Barbosa. “Benchmarking flexible job-shop scheduling and control systems”. Control Engineering Practice, vol. 21(9), pp. 1204–1225, 2013.
  • [13] M.W. Sari, I.B. Dharma, A.E. Tontowi, “Integrated Production System on Social Manufacturing: A Simulation Study”. Management Systems in Production Engineering, vol. 30(3), pp. 230–237, 2022.
  • [14] A. Ma, A. Nassehi, C. Snider. “Anarchic manufacturing: implementing fully distributed control and planning in assembly”. Production & Manufacturing Research, vol. 9(1), pp. 56–80, 2021.
  • [15] J. Huckert. “Analysis and evaluation of multi-agent systems for digital production planning and control”. Ph.D. thesis, Technical University of Kaiserslautern, Germany, 2021.
  • [16] N. Ghazi, G.Z. Rey, A. Bekrar, D. Trentesaux, M. Tadjine. “A preliminary study on integrating operation flexibility within semi-heterarchical FMS control”. International Conference on Industrial Engineering and Systems Management (IESM) pp. 1310–1317. IEEE, 2015.
  • [17] S. Mayer, C. Arnet, D. Gankin, C. Endisch. “Standardized framework for evaluating centralized and decentralized control systems in modular assembly systems”. In Proc. IEEE international conference on systems, man and cybernetics (SMC), pp. 113–119, 2019.
  • [18] R. Glawar, F. Ansari, C. Kardos, K. Matyas, W. Sihn. “Conceptual design of an integrated autonomous production control model in association with a prescriptive maintenance model (PriMa)”. Procedia CIRP, 80, pp. 482–487, 2019.
  • [19] T.T. Mezgebe, G. Demesure, H. Bril El Haouzi, R. Pannequin, A. Thomas. “CoMM: a consensus algorithm for multiagent-based manufacturing system to deal with perturbation”. The International Journal of Advanced Manufacturing Technology, vol. 105, pp. 3911–3926, 2019.
  • [20] M.A. Dittrich, S. Fohlmeister. Cooperative multi-agent system for production control using reinforcement learning. CIRP Annals, vol. 69(1), pp. 389–392, 2020.
  • [21] G. Guizzi, S. Vespoli, A. Grassi, L.C. Santillo. “Simulation-based performance assessment of a new job-shop dispatching rule for the semi-heterarchical industry 4.0 architecture”. In 2020 Winter Simulation Conference (WSC) pp. 1664–1675. IEEE, 2020.
  • [22] M.C. May, L. Kiefer, A. Kuhnle, N. Stricker, G. Lanza. “Decentralized multi-agent production control through economic model bidding for matrix production systems”. Procedia Cirp, vol. 96, pp. 3–8, 2021.
  • [23] J.B. Didden, Q.V. Dang, I.J. Adan. “A semi-decentralized control architecture for high-mix-low-volume factories in Industry 4.0”. Manufacturing Letters, vol. 30, pp. 11–14, 2021.
  • [24] A.J. Ebufegha. “Decentralized Scheduling Using the Multi-Agent System Approach for Smart Manufacturing Systems: Investigation and Design”. Ph.D. thesis, University of Calgary, Canada, 2023.
  • [25] D.K. Ismayyir, L.M. Dawood, M.M.H. AL-Khafaji, (in press), “Modelling and control architectures of production systems: literature review”. In: The 4th al. – Noor international conference for science and technology, 4NICST2022, on August,17–18, Istanbul, Turkey, 2022.
  • [26] E. Salatiello, S. Vespoli, G. Guizzi, A. Grassi. “Long-Sighted Dispatching Rules for Manufacturing Scheduling Problem in I4. 0 Decentralized Approach”. Available at SSRN 447092, 2023.
  • [27] S.L.L. Wynn, T. Boonraksa, P. Boonraksa, W. Pinthurat, B. Marungsri. “Decentralized energy management system in microgrid considering uncertainty and demand response”. Electronics, vol. 12(1), pp. 237, 2023.
  • [28] H. Zhao, H. Wang, B. Niu, X. Zhao, N. Xu. “Adaptive fuzzy decentralized optimal control for interconnected nonlinear systems with unmodeled dynamics via mixed data and event driven method”. Fuzzy Sets and Systems, 474, 108735. 2024.
  • [29] D. Chen, K. Zhang, Y. Wang, X. Yin, Z. Li, D. Filev. “Commu-nication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control”. IEEE Transactions on Intelligent Vehicles. 2024.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-19a952fb-ef6f-4247-bab0-ce774b0506d7
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