Geodesic
Model predictive control of dynamic systems with mixed uncertainty and its application to supply chain management
Dalʹnevostočnyj matematičeskij žurnal, Tome 22 (2022) no. 2, pp. 152-157.

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The paper is devoted to a discrete-time linear system with constraints on states and control inputs under conditions of interval and stochastic uncertainty. We use the model predictive control approach and get the optimal control strategy that brings the system to a setpoint. The developed results are applied to the inventory control problem in a supply chain. A numerical example is studied. 
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E. V. Chausova. Model predictive control of dynamic systems with mixed uncertainty and its application to supply chain management. Dalʹnevostočnyj matematičeskij žurnal, Tome 22 (2022) no. 2, pp. 152-157. http://geodesic.mathdoc.fr/item/DVMG_2022_22_2_a2/

[1] M. Schwenzer, M. Ay, T. Bergs, D. Abel, “Review on model predictive control: an engineering perspective”, Int J Adv Manuf Technol, 117 (2021), 1327–1349 | DOI

[2] Ramon E. Moore, R. Baker Kearfott, Michael J. Cloud, Introduction to Interval Analysis, SIAM, Philadelphia, PA, 2009 | MR

[3] N. Gunantara, “A review of multi-objective optimization: Methods and its applications”, Cogent Engineering, 5:1 papernumber 1502242 (2018) | DOI

[4] F. Blanchini, F. Rinaldi, W. Ukovich, “Least inventory control of multistorage systems with non-stochastic unknown demand”, IEEE Transaction on Robotics and Automation, 13:5 (1997), 633–645 | DOI

[5] E. V. Chausova, “Dynamic network inventory control model with interval nonstationary demand uncertainty”, Numerical Algorithms, 37:1-4 SPEC. ISS. (2004), 71–84 | DOI | MR

[6] R. B. Kearfott, M. T. Nakao, A. Neumaier, S. M. Rump, S. P. Shary, P. van Hentenryck, “Standardized notation in interval analysis”, Computational Technologies, 15:1 (2010), 7–13

[7] S. P. Shary, Numerical computation of formal solutions to interval linear systems of equations, 15 Mar 2019, arXiv: 1903.10272v1