A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 10
Oct.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
P. Ignaciuk, "Distributed Order-Up-To Inventory Control in Networked Supply Systems With Delay," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1709-1714, Oct. 2021. doi: 10.1109/JAS.2021.1004147
Citation: P. Ignaciuk, "Distributed Order-Up-To Inventory Control in Networked Supply Systems With Delay," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1709-1714, Oct. 2021. doi: 10.1109/JAS.2021.1004147

Distributed Order-Up-To Inventory Control in Networked Supply Systems With Delay

doi: 10.1109/JAS.2021.1004147
More Information
  • In this work, the dynamics of networked goods distribution systems subject to the control of a continuous-review order-up-to inventory policy are investigated. In the analytical study, as opposed to the earlier models constrained to the serial and arborescent interconnection structures, an arbitrary multi-echelon topology is considered. This external, uncertain demand, following any distribution, may be imposed on all network nodes, not just conveniently selected contact points. As in the physical systems, stock relocation to refill the reserves is subject to non-negligible delay, which poses a severe stability threat and may lead to cost-inefficient decisions. A state-space model is created and used as the framework for analyzing system properties. In particular, it is formally demonstrated that despite unpredictable demand fluctuations, a feasible (nonnegative and bounded) reserves replenishment signal is generated at all times, and the stock gathered at the nodes does not surpass a finite, precisely determined level. The theoretical content is illustrated with a case study of the Chinese oil supply system.


  • loading
  • [1]
    G. Xiong, X. S. Dong, H. Lu, and D. Y. Shen, “Research progress of parallel control and management,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 355–367, Mar. 2020. doi: 10.1109/JAS.2019.1911792
    X.-M. Zhang, Q.-L. Han, X. H. Ge, D. R. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: A survey of trends and techniques,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 1–17, Jan. 2020. doi: 10.1109/JAS.2019.1911861
    P. Ignaciuk and Ł. Wieczorek, “Networked base-stock inventory control in complex distribution systems,” Math. Probl. Eng., vol. 2019, pp. 1–14, 2019.
    X. J. Zheng, M. X. Yin, and Y. X. Zhang, “Integrated optimization of location, inventory and routing in supply chain network design,” Transport Res. B, vol. 121, pp. 1–20, 2019. doi: 10.1016/j.trb.2019.01.003
    Y. He, S. Y. Li, and Y. Zheng, “Distributed state estimation for leak detection in water supply networks,” IEEE/CAA J. Autom. Sinica, pp. 1–9, 2017. DOI: 10.1109/JAS.2017.7510367.
    M. V. Basin, F. Guerra-Avellaneda, and Y. B. Shtessel, “Stock management problem: Adaptive fixed-time convergent continuous controller design,” IEEE Trans. Syst. Man Cy.-S, vol. 50, no. 12, pp. 4974–4983, 2019. doi: 10.1109/TSMC.2019.2930563
    R. Abbou, J. J. Loiseau, and Ch. Moussaoui, “Robust inventory control of production systems subject to uncertainties on demand and lead times,” Int. J. Prod. Res., vol. 55, no. 8, pp. 2177–2196, 2017. doi: 10.1080/00207543.2016.1214295
    P. Ignaciuk, “Discrete-time control of production-inventory systems with deteriorating stock and unreliable supplies,” IEEE Trans. Syst. Man Cy.-S., vol. 45, no. 2, pp. 338–348, Feb. 2015. doi: 10.1109/TSMC.2014.2347012
    J. B. Sheu and T. Kundu, “Forecasting time-varying logistics distribution flows in the one belt-one road strategic context,” Transport Res. E, vol. 117, pp. 5–22, 2018. doi: 10.1016/j.tre.2017.03.003
    P. Leśniewski and A. Bartoszewicz, “Optimal model reference sliding mode control of perishable inventory systems,” IEEE Trans. Autom. Sci. Eng, vol. 17, no. 3, pp. 1647–1656, 2020. doi: 10.1109/TASE.2020.2969493
    S. Axsäter, Inventory Control, 3E. Berlin Heidelberg: Springer, 2015.
    H. Sarimveis, P. Patrinos, C. D. Tarantilis, and C. T. Kiranoudis, “Dynamic modeling and control of supply chain systems: A review,” Comp. Oper. Res., vol. 35, no. 11, pp. 3530–3561, 2008. doi: 10.1016/j.cor.2007.01.017
    K. Hoberg, J. R. Bradley, and U. W. Thonemann, “Analyzing the effect of the inventory policy on order and inventory variability with linear control theory,” Eur. J. Oper. Res., vol. 176, no. 3, pp. 1620–1642, 2007. doi: 10.1016/j.ejor.2005.10.040
    P. Ignaciuk, “Discrete inventory control in systems with perishable goods – a time-delay system perspective,” IET Control Theory Appl., vol. 8, no. 1, pp. 11–21, 2014. doi: 10.1049/iet-cta.2013.0636
    K. Hoberg and U. W. Thonemann, “Analyzing variability, cost, and responsiveness of base-stock inventory policies with linear control theory,” IIE Trans., vol. 47, no. 8, pp. 865–879, 2015.
    M. M. Naim, V. L. Spiegler, J. Wikner, and D. R. Towill, “Identifying the causes of the bullwhip effect by exploiting control block diagram manipulation with analogical reasoning,” Eur. J. Oper. Res., vol. 263, pp. 240–246, 2017. doi: 10.1016/j.ejor.2017.05.014
    M. Boccadoro, F. Martinelli, and P. Valigi, “Supply chain management by H-infinity control,” IEEE Trans. Autom. Sci. Eng., vol. 5, no. 4, pp. 703–707, Oct. 2008. doi: 10.1109/TASE.2008.917152
    D. F. Fu, C. M. Ionescu, El-H. Aghezzaf, and R. De Keyser, “A constrained EPSAC approach to inventory control for a benchmark supply chain system,” Int. J. Prod. Res., vol. 54, no. 1, pp. 232–250, 2016. doi: 10.1080/00207543.2015.1070214
    P. Ignaciuk, “Dead-time compensation in continuous-review perishable inventory systems with multiple supply alternatives,” J. Proc. Contr., vol. 22, no. 5, pp. 915–924, 2012. doi: 10.1016/j.jprocont.2012.03.006
    R. Dominguez, S. Cannella, and J. M. Framinan, “The impact of the supply chain structure on bullwhip effect,” Appl. Math. Model., vol. 39, no. 23–24, pp. 7309–7325, 7309.
    S. M. Zahraei and Ch.-Ch. Teo, “Optimizing a supply network with production smoothing, freight expediting and safety stocks: An analysis of tactical trade-offs,” Eur. J. Oper. Res., vol. 262, pp. 75–88, 2017. doi: 10.1016/j.ejor.2017.02.045
    Y. S. Wei and S. Y. Li, “Water supply networks as cyber-physical systems and controllability analysis,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 3, pp. 313–319, Jul. 2015. doi: 10.1109/JAS.2015.7152666
    C. Grob, Inventory Management in Multi-Echelon Networks. On the Optimization of Reorder Points. Wiesbaden: Springer, 2019.
    K. D. Cattani, F. R. Jacobs, and J. Schoenfelder, “Common inventory modeling assumptions that fall short: Arborescent networks, poisson demand, and single-echelon approximations,” J. Oper. Manag., vol. 29, no. 5, pp. 488–499, 2011. doi: 10.1016/j.jom.2010.11.008
    G. Van der Heide, P. Buijs, K. J. Roodbergen, and I. F. A. Vis, “Dynamic shipments of inventories in shared warehouse and transportation networks,” Transport Res. E, vol. 118, pp. 240–257, 2018. doi: 10.1016/j.tre.2018.07.012
    P. Ignaciuk, “Nonlinear inventory control with discrete sliding modes in systems with uncertain delay,” IEEE Trans. Ind. Inf., vol. 10, no. 1, pp. 559–568, Feb. 2014. doi: 10.1109/TII.2013.2278476
    X.-M. Zhang, Q.-L. Han, A. Seuret, F. Gouaisbaut, and Y. He, “Overview of recent advances in stability of linear systems with time-varying delays,” IET Control Theory Appl., vol. 13, no. 1, pp. 1–16, 2019. doi: 10.1049/iet-cta.2018.5188
    X.-M. Zhang, Q.-L. Han, and X. H. Ge, “Novel stability criteria for linear time-delay systems using Lyapunov-Krasovskii functionals with a cubic polynomial on time-varying delay,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 77–85, Jan. 2021.


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (883) PDF downloads(46) Cited by()


    • A model of multi-echelon logistic networks exhibiting complex topology is constructed
    • Goods reflow is subject to positive delay
    • Uncertain demand is placed at any network node
    • Distributed order-up-to inventory policy controls stock replenishment
    • Formal analysis is supported with numerical study of Chinese oil supply system


    DownLoad:  Full-Size Img  PowerPoint