Open Access
RAIRO-Oper. Res.
Volume 57, Number 1, January-February 2023
Page(s) 17 - 42
Published online 12 January 2023
  • K.J. Arrow, T. Harris and J. Marschak, Optimal inventory policy. Econometrica 19 (1951) 250–272. [Google Scholar]
  • L. Benkherouf and B.H. Gilding, Optimal policies for a deterministic continuous-time inventory model with several suppliers. RAIRO:RO 55 (2021) S947–S966. [Google Scholar]
  • J. Bezanson, A. Edelman, S. Karpinski and V.B. Shah, Julia: A fresh approach to numerical computing. SIAM Rev. 59 (2017) 65–98. [Google Scholar]
  • A. Bhaya, E. Kaszkurewicz and L.C. Roth, One step ahead optimal control of a single echelon supply chain using mathematical programming, in Proceedings of the 9th International Conference on Operations Research and Enterprise Systems (ICORES), Valletta, Malta (2020) 233–240. [Google Scholar]
  • M.D. Canon, C.D. Cullum and E. Polak, Theory of Optimal Control and Mathematical Programming, 1st edition. New York, NY, USA, McGraw-Hill (1970). [Google Scholar]
  • R.C. Dorf and R.H. Bishop, Modern Control Systems, 8th edition. Boston, MA, USA, Addison Wesley Longmann (1998). [Google Scholar]
  • I. Dunning, J. Huchette and M. Lubin, JuMP: A modeling language for mathematical optimization. SIAM Rev. 59 (2017) 295–320. [Google Scholar]
  • K. Hoberg, J. Bradley and U. Thonemann, Analyzing the effect of the inventory policy on order and inventory variability with linear control theory. Eur. J. Oper. Res. 176 (2007) 1620–1642. [CrossRef] [Google Scholar]
  • D. Ivanov, S.P. Sethi, A. Dolgui and B. Sokolov, A survey on control theory applications to operational systems, supply chain management, and industry 4.0. Annu. Rev. Control 46 (2018) 134–147. [CrossRef] [Google Scholar]
  • E. Lavretsky, Greedy optimal control, in Proceedings of the American Control Conference (ACC), Chicago, IL, USA (2000) 3888–3892. [Google Scholar]
  • J. Lin and M. Naim, Why do nonlinearities matter? The repercussions of linear assumptions on the dynamic behavior of assemble-to-order systems. Int. J. Prod. Res. 57 (2019) 6424–6451. [CrossRef] [Google Scholar]
  • J. Lin, M. Naim, L. Purvis and J. Gosling, The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015. Int. J. Prod. Econ. 194 (2017) 135–152. [CrossRef] [Google Scholar]
  • J. Lin, M. Naim and V. Spiegler, Delivery time dynamics in an assemble-to-order inventory and order based production control system. Int. J. Prod. Econ. 223 (2020) 107531. [CrossRef] [Google Scholar]
  • B. McGarvey and B. Hannon, Dynamic Modeling for Business Management: an Introduction, 1st edition. New York, NY, USA, Springer (2004). [CrossRef] [Google Scholar]
  • J.A. Muckstadt and A. Sapra, Principles of Inventory Management: When you are Down to Four, Order More, 1st edition. New York, NY, USA, Springer (2010). [Google Scholar]
  • K.G. Murty, Forecasting for supply chain and portfolio management (2006). Accessed on: Feb. 16, 2022, [Online] Available: [Google Scholar]
  • E. Perea-López, B.E. Ydstie and I. Grossmann, A model predictive control strategy for supply chain optimization. Comput. Chem. Eng. 27 (2003) 1201–1218. [CrossRef] [Google Scholar]
  • J. Qin, Y. Chow, J. Yang and R. Rajagopal, Online modified greedy algorithm for storage control under uncertainty. IEEE Trans. Power Syst. 31 (2016) 1729–1743. [CrossRef] [Google Scholar]
  • J. Schwartz, W.-L. Wang and D. Rivera, Simulation-based optimization of process control policies for inventory management in supply chains. Automatica 42 (2006) 1311–1320. [CrossRef] [Google Scholar]
  • S.P. Sethi and G. Thompson, Optimal Control Theory: Applications to Management Science and Economics, 2nd edition. New York, NY, USA, Springer (2000). [Google Scholar]
  • J. Simon, M. Naim and D.R. Towill, Dynamic analysis of a WIP compensated decision support system. Int. J. Manuf. Syst. 1 (1994) 283–297. [Google Scholar]
  • V. Spiegler, M. Naim and J. Wikner, A control engineering approach to the assessment of supply chain resilience. Int. J. Prod. Res. 50 (2012) 6162–6187. [CrossRef] [Google Scholar]
  • H. Stadtler, C. Kilger and H. Meyr, Supply Chain Management and Advanced Planning, 5th edition. Berlin Heidelberg, Germany, Springer-Verlag (2015). [Google Scholar]
  • J.M. Stern, J.S. Shiele and I. Ross, The EVA Challenge: Implementing Value Added Change in an Organization, 1st edition. New York, NY, USA, John Wiley & Sons (2001). [Google Scholar]
  • W.J. Stevenson, Operations Management, 14th edition. New York, NY, USA, McGraw-Hill (2021). [Google Scholar]
  • K. Subramanian, J. Rawlings and C. Maravelias, Economic model predictive control for inventory management in supply chains. Comput. Chem. Eng. 64 (2014) 71–80. [CrossRef] [Google Scholar]
  • H. Tang, H. Zhang, R. Liu and Y. Du, Integrating multi-index materials classification and inventory control in discrete manufacturing industry: Using a hybrid ABC-chaos algorithm. IEEE Trans. Eng. Manag. (2020) 1–18. [Google Scholar]
  • F. Tao, T. Fan and K.-K. Lai, Inventory management under power structures with consignment contract subject to inventory inaccuracy. IEEE Trans. Eng. Manag. 66 (2019) 763–773. [CrossRef] [Google Scholar]
  • T. Tosetti, D. Patiño, F. Capraro and A. Gambier, A new inventory level APIOBPCS-based controller, in Proceedings of the American Control Conference (ACC). Seattle, WA, USA (2008) 2886–2891. [Google Scholar]
  • E. Trélat and E. Zuazua, The turnpike property in finite-dimensional nonlinear optimal control. J. Differ. Equ. 258 (2015) 81–114. [CrossRef] [Google Scholar]
  • W.-L. Wang, D. Rivera and K. Kempf, Model predictive control strategies for supply chain management in semiconductor manufacturing. Int. J. Prod. Econ. 107 (2007) 56–77. [CrossRef] [Google Scholar]
  • Z. Wang, T.S. Felix and M. Li, A robust production control policy for hedging against inventory inaccuracy in a multiple-stage production system with time delay. IEEE Trans. Eng. Manag. 65 (2018) 474–486. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.