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Data Driven Inventory Consensus Control for a Supply Chain System with a Design Change Cover

Data Driven Inventory Consensus Control for a Supply Chain System with a Design Change

Open Access
|Dec 2025

References

  1. Ahn, H.S., Chen, Y. and Moore, K.L. (2007). Iterative learning control: Brief survey and categorization, IEEE Transactions on Systems, Man, and Cybernetics C: Applications and Reviews 37(6): 1099–1121, DOI: 10.1109/TSMCC.2007.905759.
  2. Behzadi, G., Sullivan, M.J.O., Olsen, T. and Zhang, A. (2018). Allocation flexibility for agribusiness supply chains under market demand disruption, International Journal of Production Research 56(10): 3524–3546, DOI: 10.1080/00207543.2017.1349955.
  3. Bu, X., Wang, Q., Hou, Z. and Qian, W. (2018). Data driven control for a class of nonlinear systems with output saturation, ISA Transactions 81: 1–7, DOI: 10.1016/j.isatra.2018.07.009.
  4. Bu, X., Yu, Q., Hou, Z. and Qian, W. (2019). Model free adaptive iterative learning consensus tracking control for a class of nonlinear multiagent systems, IEEE Transactions on Systems, Man & Cybernetics: Systems 49(4): 677–686, DOI: 10.1109/TSMC.2017.2734799.
  5. Chan, F.T. and Zhang, T. (2011). The impact of collaborative transportation management on supply chain performance: A simulation approach, Expert Systems with Applications 38(3): 2319–2329, DOI:10.1016/j.eswa.2010.08.020.
  6. Chi, R., Wang, D., Hou, Z. and Jin, S. (2012). Data-driven optimal terminal iterative learning control, Journal of Process Control 22(10): 2026–2037.
  7. Clarkson, P.J., Simons, C. and Eckert, C. (2004). Predicting change propagation in complex design, Journal of Mechanical Design 126(5): 788–797, DOI:10.1115/1.1765117.
  8. Clempner, J.B. and Poznyak, A.S. (2023). Computing a mechanism for a Bayesian and partially observable Markov approach, International Journal of Applied Mathematics and Computer Science 33(3): 463–478, DOI: 10.34768/amcs-2023-0034.
  9. Coopmans, I., Bijttebier, J., Marchand, F., Mathijs, E., Messely, L., Rogge, E., Sanders, A. and Wauters, E. (2021). COVID-19 impacts on flemish food supply chains and lessons for agri-food system resilience, Agricultural Systems 190: 103136, DOI: 10.1016/j.agsy.2021.103136.
  10. Darmawan, A., Wong, H., Thorstenson, A. and Talley, W. (2021). Supply chain network design with coordinated inventory control, Transportation Research E: Logistics and Transportation Review 145: 102168, DOI: 10.1016/j.tre.2020.102168.
  11. Eckert, C., Clarkson, P.J. and Zanker, W. (2004). Change and customisation in complex engineering domains, Research in Engineering Design 15(1): 1–21, DOI:10.1007/s00163-003-0031-7.
  12. Farrera, B., López-Estrada, F.-R., Chadli, M., Valencia-Palomo, G. and Gómez-Peñate, S. (2020). Distributed fault estimation of multi-agent systems using a proportional-integral observer: A leader-following application, International Journal of Applied Mathematics and Computer Science 30(3): 551–560, DOI: 10.34768/amcs-2020-0040.
  13. Ge, S.S., Hang, C.C., Lee, T.H. and Zhang, T. (2013). Stable Adaptive Neural Network Control, Springer Science & Business Media, New York.
  14. Gomi, H. and Kawato, M. (1993). Neural network control for a closed-loop system using feedback-error-learning, Neural Networks 6(7): 933–946, DOI:10.1016/S0893-6080(09)80004-X.
  15. González, A., Sala, A. and Armesto, L. (2022). Decentralized multi-agent formation control with pole-region placement via cone-complementarity linearization, International Journal of Applied Mathematics and Computer Science 32(3): 415–428, DOI: 10.34768/amcs-2022-0030.
  16. Hou, Z.S. (1994). The Parameter Identification, Adaptive Control and Model Free Learning Adaptive Control for Nonlinear Systems, Ph.D. thesis, Northeastern University, Shenyang.
  17. Hou, Z. and Jin, S. (2010). A novel data-driven control approach for a class of discrete-time nonlinear systems, IEEE Transactions on Control Systems Technology 19(6): 1549–1558.
  18. Hou, Z.S. and Jin, S.T. (2013). Model Free Adaptive Control: Theory and Applications, CRC Press, Boca Raton, DOI: 10.1201/b15752.
  19. Hou, Z.S. and Wang, Z. (2013). From model-based control to data-driven control: Survey, classification and perspective, Information Sciences 235: 3–35.
  20. Ignaciuk, P. (2015). Discrete-time control of production-inventory systems with deteriorating stock and unreliable supplies, IEEE Transactions on Systems, Man and Cybernetics: Systems 45(2): 338–348, DOI: 10.1109/TSMC.2014.2347012.
  21. Ivanov, D., Dolgui, A. and Sokolov, B. (2019a). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics, International Journal of Production Research 57(3): 829–846, DOI: 10.1080/00207543.2018.1488086.
  22. Ivanov, D., Dolgui, A. and Sokolov, B. (2019b). Ripple effect in the supply chain: Definitions, frameworks and future research perspectives, in D. Ivanov et al. (Eds), Handbook of Ripple Effects in the Supply Chain, Springer, Cham, pp. 1–33, DOI: 10.1007/978-3-030-14302-21.
  23. Jarratt, T., Eckert, C.M., Caldwell, N.H. and Clarkson, P.J. (2011). Engineering change: An overview and perspective on the literature, Research in Engineering Design 22(2): 103–124, DOI: 10.1007/s00163-010-0097-y.
  24. Lee, H.L., So, K.C. and Tang, C.S. (2000). The value of information sharing in a two-level supply chain, Management Science 46(5): 626–643, DOI: 10.1287/MNSC.46.5.626.12047.
  25. Li, Q.K., Gao, X.F., Peng, C., Zhang, Y.L. and Yi, J. (2023). Data-driven change control design for product and supply chain synchronous evolution systems under cyber-attacks, Scientia Sinica Informationis 53: 325–343, DOI: 10.1360/SSI-2021-0435, (in Chinese).
  26. Li, Q.K., Lin, H., Tan, X. and Du, S. (2020). H∞ consensus for multiagent-based supply chain systems under switching topology and uncertain demands, IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(12): 4905–4918, DOI: 10.1109/TSMC.2018.2884510.
  27. Liang, J., Bu, X., Cui, L. and Hou, Z. (2021). Data-driven bipartite formation for a class of nonlinear MIMO multiagent systems, IEEE Transactions on Neural Networks and Learning Systems 34(6): 3161–3173, DOI: 10.1109/TNNLS.2021.3111893.
  28. Liu, Z., Jahanshahi, H., Volos, C., Bekiros, S., He, S., Alassafi, M.O. and Ahmad, A.M. (2021). Distributed consensus tracking control of chaotic multiagent supply chain network: A new fault-tolerant, finite-time, and chatter-free approach, Entropy 24(1): 33, DOI: 10.3390/e24010033.
  29. Luo, J., Yang, W., Ju, G. and Min, X. (2014). The study on consensus control of supply chain system based on multi-agent model, Proceedings of the 2014 International Conference on Advanced Mechatronic Systems, Kumamoto, Japan, pp. 526–531, DOI: 10.1109/ICAMechS.2014.6911602.
  30. López-Estrada, F.-R., Darias, H., Puig, V., Valencia-Palomo, G., Domínguez-Zenteno, J. and Guerrero-Sánchez, M.-E. (2024). Cooperative convex control of multiagent systems applied to differential drive robots, International Journal of Applied Mathematics and Computer Science 34(2): 199–210, DOI: 10.61822/amcs-2024-0014.
  31. Notarnicola, I., Bin, M., Marconi, L. and Notarstefano, G. (2023). The gradient tracking is a distributed integral action, IEEE Transactions on Automatic Control 68(12): 7911–7918, DOI: 10.1109/TAC.2023.3248487.
  32. Patalas-Maliszewska, J., Posdzich, M. and Skrzypek, K. (2022). Modelling information for the burnishing process in a cyber-physical production system, International Journal of Applied Mathematics and Computer Science 32(3): 345–354, DOI: 10.34768/amcs-2022-0025.
  33. Patalas-Maliszewska, J., Wiśniewski, R., Zhou, M., Topczak, M. and Wojnakowski, M. (2024). Applying additive manufacturing technologies to a supply chain: A Petri net-based decision model, International Journal of Applied Mathematics and Computer Science 34(3): 513–525, DOI: 10.61822/amcs-2024-0035.
  34. Peng, C., Zhang, A. and Li, J. (2021). Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties, International Journal of Applied Mathematics and Computer Science 31(4): 635–645, DOI: 10.34768/amcs-2021-0044.
  35. Rodrigues, L. and Boukas, E.K. (2006). Piecewise-linear H∞ controller synthesis with applications to inventory control of switched production systems, Automatica 42(8): 1245–1254, DOI: 10.1016/j.automatica.2006.04.004.
  36. Sarimveis, H., Patrinos, P., Tarantilis, C.D. and Kiranoudis, C.T. (2008). Dynamic modeling and control of supply chain systems: A review, Computers & Operations Research 35(11): 3530–3561.
  37. Snyder, L.V. and Shen, Z.M. (2019). Applications of supply chain theory, in L.V. Snyder and Z.M. Shen (Eds), Fundamentals of Supply Chain Theory, John Wiley & Sons, Hoboken, pp. 615–642, DOI: 10.1002/9781119584445.ch16.
  38. Ullah, I., Tang, D. and Yin, L. (2016). Engineering product and process design changes: A literature overview, Procedia CIRP 56: 25–33, DOI: 10.1016/j.procir.2016.10.010.
  39. Wang, Z., Wang, D., Lian, J., Ge, H. and Wang, W. (2024). Momentum-based distributed gradient tracking algorithms for distributed aggregative optimization over unbalanced directed graphs, Automatica 164: 111596.
  40. Wright, I. (1997). A review of research into engineering change management: Implications for product design, Design Studies 18(1): 33–42, DOI: 10.1016/50142-694X(96)00029-4.
  41. Xiong, S. and Hou, Z. (2021). Data-driven formation control for unknown mimo nonlinear discrete-time multi-agent systems with sensor fault, IEEE Transactions on Neural Networks and Learning Systems 33(12): 7728–7742, DOI: 10.1109/TNNLS.2021.3087481.
  42. Xu, L., Mak, S. and Brintrup, A. (2021). Will bots take over the supply chain? Revisiting agent-based supply chain automation, International Journal of Production Economics 241: 108279, DOI: 10.1016/j.ijpe.2021.108279.
  43. Zhao, K., Scheibe, K., Blackhurst, J. and Kumar, A.K. (2019). Supply chain network robustness against disruptions: Topological analysis, measurement, and optimization, IEEE Transactions on Engineering Management 66(1): 127–139, DOI: 10.1109/TEM.2018.2808331.
  44. Zhao, L. and Yu, J. (2017). Adaptive bipartite consensus tracking control for coopetition multi-agent systems with input saturation, 6th Data Driven Control and Learning Systems Conference, Chongqing, China, pp. 383–386, DOI: 10.1109/DDCLS.2017.8068102.
DOI: https://doi.org/10.61822/amcs-2025-0050 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 703 - 717
Submitted on: Mar 30, 2025
Accepted on: Sep 7, 2025
Published on: Dec 15, 2025
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Lingling Fan, Xiangchen Zeng, Shuangshuang Xiong, Qingkui Li, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.