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Inventory control for the supply chain: An adaptive control approach based on the identification of the lead-time

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  • Garcia, C.A.
  • Ibeas, A.
  • Herrera, J.
  • Vilanova, R.
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    Abstract

    In this paper, an Internal Model Control (IMC) scheme is incorporated in production inventory control systems in a complete supply chain. This control scheme presents a good target inventory tracking under the perfect knowledge of the system. Furthermore, the inventory tracking and load disturbance rejection control problems can be tackled separately. However, the closed-loop performance of the IMC scheme may be degraded due to a mismatch between the modelled and actual delay or to the fact that delays may be time-varying. Thus, the IMC control scheme is enhanced in this work with a novel method for the online identification of lead times based on a multimodel scheme. In this way, all benefits of the IMC scheme can be exploited. A detailed discussion of the proposed production inventory system is provided including a stability and performance analysis as well as the identification capabilities of the algorithm. Several simulation examples illustrate the efficiency of the approach.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 40 (2012)
    Issue (Month): 3 ()
    Pages: 314-327

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    Handle: RePEc:eee:jomega:v:40:y:2012:i:3:p:314-327

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    Keywords: Supply chain; Inventory control; Adaptive control;

    References

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    1. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2004. "The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective," European Journal of Operational Research, Elsevier, vol. 153(3), pages 727-750, March.
    2. Hoberg, Kai & Bradley, James R. & Thonemann, Ulrich W., 2007. "Analyzing the effect of the inventory policy on order and inventory variability with linear control theory," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1620-1642, February.
    3. Tang, Dong, 2011. "Managing finished-goods inventory under capacitated delayed differentiation," Omega, Elsevier, vol. 39(5), pages 481-492, October.
    4. Amini, Mehdi & Li, Haitao, 2011. "Supply chain configuration for diffusion of new products: An integrated optimization approach," Omega, Elsevier, vol. 39(3), pages 313-322, June.
    5. Silver, Edward A. & Bischak, Diane P., 2011. "The exact fill rate in a periodic review base stock system under normally distributed demand," Omega, Elsevier, vol. 39(3), pages 346-349, June.
    6. Balan, S. & Vrat, Prem & Kumar, Pradeep, 2009. "Information distortion in a supply chain and its mitigation using soft computing approach," Omega, Elsevier, vol. 37(2), pages 282-299, April.
    7. Ouyang, Yanfeng & Li, Xiaopeng, 2010. "The bullwhip effect in supply chain networks," European Journal of Operational Research, Elsevier, vol. 201(3), pages 799-810, March.
    8. Disney, S. M. & Towill, D. R., 2003. "On the bullwhip and inventory variance produced by an ordering policy," Omega, Elsevier, vol. 31(3), pages 157-167, June.
    9. Schwartz, Jay D. & Rivera, Daniel E., 2010. "A process control approach to tactical inventory management in production-inventory systems," International Journal of Production Economics, Elsevier, vol. 125(1), pages 111-124, May.
    10. Sriver, Todd A. & Chrissis, James W. & Abramson, Mark A., 2009. "Pattern search ranking and selection algorithms for mixed variable simulation-based optimization," European Journal of Operational Research, Elsevier, vol. 198(3), pages 878-890, November.
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    Cited by:
    1. Fang, Xin & Zhang, Cheng & Robb, David J. & Blackburn, Joseph D., 2013. "Decision support for lead time and demand variability reduction," Omega, Elsevier, vol. 41(2), pages 390-396.
    2. Garcia Salcedo, Carlos Andres & Ibeas Hernandez, Asier & Vilanova, Ramón & Herrera Cuartas, Jorge, 2013. "Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches," European Journal of Operational Research, Elsevier, vol. 224(2), pages 261-272.

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