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Performance Evaluation of a Production Control Architectures for Flexible Manufacturing System

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The study offers an analytical approach for assessing production control system performance in terms of volume and variety features of the product being manufactured for the Flexible Manufacturing System. Throughput, resource utilisation, cycle time, and maximum completion time are the four performance indicators taken into account. The objective is to quantify the production system's performance and define the reactive capability of production control architectures in a market that is becoming increasingly competitive. The examination of control systems' production performances motivates the evaluation of these architectures through the introduction of scheduling approaches that deal with uncertainty. Results revealed the semi-heterarchical control architecture outperforms the hierarchical control architecture through multiple performance criteria. A case study with regard to a manufacturing control system is presented in order to highlight the significance of our methodology and the contribution of the research.
Twórcy
  • Production Engineering and Metallurgy Department, University of Technology, 35010 Iraq
  • Production Engineering and Metallurgy Department, University of Technology, 35010 Iraq
  • Production Engineering and Metallurgy Department, University of Technology, 35010 Iraq
Bibliografia
  • 1. Pfeifer, M.R. Operative Production Controlling as Entrance into Controlling 4.0. Trends Economics and Management 2021; 15(37): 73–84.
  • 2. Liu, Y.F., Zhang, Q.S. Multi-objective production planning model for equipment manufacturing enerprises with multiple uncertainties in demand. Advances in Production Engineering & Management 2018; 13(4): 429–441.
  • 3. Eyers, D.R., Potter, A.T., Gosling, J., Naim, M.M. The flexibility of industrial additive manufacturing systems. International Journal of Operations & Production Management 2018; 38(12): 2313–2343.
  • 4. Zbib, N., Pach, C., Sallez, Y., Trentesaux, D. Heterarchical production control in manufacturing sysems using the potential fields concept. Journal of Intelligent Manufacturing 2012; 23: 1649–1670.
  • 5. Rey, G.Z., Pach, C., Aissani, N., Bekrar, A., Berger, T., Trentesaux, D. The control of myopic behawior in semi-heterarchical production systems: A holonic framework. Engineering Applications of Artificial Intelligence 2013; 26(2): 800–817.
  • 6. Jimenez, J.F., Bekrar, A., Zambrano-Rey, G., Trentesaux, D., Leitão, P. Pollux: a dynamic hybrid control architecture for flexible job shop systems. International Journal of Production Research 2017; 55(15): 4229–4247.
  • 7. Jimenez, J.F., Bekrar, A., Trentesaux, D., Leitão, P. A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures. International Journal of Production Research 2016; 54(23): 7027–7042.
  • 8. Jimenez, J.F., Bekrar, A., Giret, A., Leitão, P., Trentesaux, D. A dynamic hybrid control architecture for sustainable manufacturing control. IFAC-PapersOnLine 2016; 49(31): 114–119.
  • 9. Roa, J., Jimenez, J.F., Zambrano-Rey, G. Directive mode for the semi-heterarchical control architecture of a flexible manufacturing system. IFAC-PapersOnLine 2019; 52(10): 19–24.
  • 10. Gonzalez, S.R., Zambrano, G.M., Mondragon, I.F. Semi-heterarchical architecture to AGV adjustable autonomy within FMSs. IFAC-PapersOnLine 2019; 52(10): 7–12.
  • 11. Ma, A., Nassehi, A., Snider, C. Anarchic manufacturing: implementing fully distributed control and planning in assembly. Production & Manufacturing Research 2021; 9(1): 56–80.
  • 12. Kovalenko, I., Moyne, J., Bi, M., Balta, E.C., Ma, W., Qamsane, Y., Mao Z.M., Dawn M.T. Barton, K. Toward an automated learning control architecture for cyber-physical manufacturing systems. IEEE Access 2022; 10: 38755–38773.
  • 13. Vespoli, S., Guizzi, G., Gebennini, E., Grassi, A.A. novel throughput control algorithm for semi-heterarchical industry 4.0 architecture. Annals of Operations Research 2022; 1–21.
  • 14. Salatiello, E., Vespoli, S., Guizzi, G., Grassi, A. Long-Sighted Dispatching Rules for Manufacturing Scheduling Problem in I4. 0 Decentralised Approach. Computers & Industrial Engineering. Available at SSRN 4470926 2023; 1–25.
  • 15. Feng, Q., Zhang, Y., Sun, B., Guo, X., Fan, D., Ren, Y. Yanjie, Song, Y., Wang. Z. Multi-level predictive maintenance of smart manufacturing systems driven by digital twin: A matheuristics approach. Journal of Manufacturing Systems 2023; 68: 443–454.
  • 16. Weckenborg, C., Schumacher, P., Thies, C., Spengler, T.S. Flexibility in manufacturing system design: A review of recent approaches from Operations Research. European journal of operational research 2023.
  • 17. Schumacher, P., Weckenborg, C., Spengler, T. S. The impact of operation, equipment, and material handling flexibility on the design of matrix-structured manufacturing systems. IFAC-PapersOnLine 2022; 55(2): 481–486.
  • 18. González, S.R., Mondragón, I., Zambrano, G., Hernandez, W., Montaña, H. Manufacturing control architecture for FMS with AGV: A state-of-the-art. In Advances in Automation and Robotics Research in Latin America: Proceedings of the 1st Latin American Congress on Automation and Robotics, Panama City, Panama 2017; 157–172.
  • 19. Boccella, A.R., Centobelli, P., Cerchione, R., Murino, T., Riedel, R. Evaluating centralized and heterarchical control of smart manufacturing systems in the era of Industry 4.0. Applied Sciences 2020; 10(3): 755.
  • 20. Ismayyir, D.K., Dawood, L.M., AL-Khafaji, M.M.H. (in press) Modelling and control architectures of production systems: literature review. In: The 4th al. –Noor international conference for science and tech- nology, 4NICST 2022, on August, 17–18, Istanbul.
  • 21. Ismayyir, D.K., Dawood, L.M., AL-Khafaji, M.M.H.,(in press), A Review of the developments in production control systems. In:4th international conference on sustainable engineering techniques, ICSET 2022; on October 5–6, Baghdad, Iraq.
  • 22. Zhang, Y., Zhu, H., Tang, D., Zhou, T., Gui, Y. Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robotics and Computer-Integrated Manufacturing 2022; 78: 102412.
  • 23. Mezgebe, T.T. Human-inspired algorithms for designing new control system in the context of factory of the future (Doctoral dissertation, Université de Lorraine) 2020.
  • 24. Rey, G.Z., Bonte, T., Prabhu, V., Trentesaux, D. Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach. Simulation Modelling Practice and Theory 2014; 46: 53–75.
  • 25. Trentesaux, D., Pach, C., Bekrar, A., Sallez, Y., Berger, T., Bonte, T., Barbosa, J. Benchmarking flexible job-shop scheduling and control systems. Control Engineering Practice 2013; 21(9): 1204–1225
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-75cd6e67-ae4e-416d-97db-fef8d892c760
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