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Robust optimization of uncertain multistage inventory systems with inexact data in decision rules


  • Frans J. C. T. Ruiter

    () (Tilburg University)

  • Aharon Ben-Tal

    (Technion-Israel Institute of Technology
    Shenkar College)

  • Ruud C. M. Brekelmans

    (Tilburg University)

  • Dick Hertog

    (Tilburg University)


In production-inventory problems customer demand is often subject to uncertainty. Therefore, it is challenging to design production plans that satisfy both demand and a set of constraints on e.g. production capacity and required inventory levels. Adjustable robust optimization (ARO) is a technique to solve these dynamic (multistage) production-inventory problems. In ARO, the decision in each stage is a function of the data on the realizations of the uncertain demand gathered from the previous periods. These data, however, are often inaccurate; there is much evidence in the information management literature that data quality in inventory systems is often poor. Reliance on data “as is” may then lead to poor performance of “data-driven” methods such as ARO. In this paper, we remedy this weakness of ARO by introducing a model that treats past data itself as an uncertain model parameter. We show that computational tractability of the robust counterparts associated with this extension of ARO is still maintained. The benefits of the new model are demonstrated by a numerical test case of a well-studied production-inventory problem. Our approach is also applicable to other ARO models outside the realm of production-inventory planning.

Suggested Citation

  • Frans J. C. T. Ruiter & Aharon Ben-Tal & Ruud C. M. Brekelmans & Dick Hertog, 2017. "Robust optimization of uncertain multistage inventory systems with inexact data in decision rules," Computational Management Science, Springer, vol. 14(1), pages 45-66, January.
  • Handle: RePEc:spr:comgts:v:14:y:2017:i:1:d:10.1007_s10287-016-0253-6
    DOI: 10.1007/s10287-016-0253-6

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    References listed on IDEAS

    1. A. Gürhan Kök & Kevin H. Shang, 2007. "Inspection and Replenishment Policies for Systems with Inventory Record Inaccuracy," Manufacturing & Service Operations Management, INFORMS, vol. 9(2), pages 185-205, February.
    2. Guigues, Vincent & Sagastizábal, Claudia, 2012. "The value of rolling-horizon policies for risk-averse hydro-thermal planning," European Journal of Operational Research, Elsevier, vol. 217(1), pages 129-140.
    3. Rocha, Paula & Kuhn, Daniel, 2012. "Multistage stochastic portfolio optimisation in deregulated electricity markets using linear decision rules," European Journal of Operational Research, Elsevier, vol. 216(2), pages 397-408.
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    Cited by:

    1. Yanıkoğlu, İhsan & Gorissen, Bram L. & den Hertog, Dick, 2019. "A survey of adjustable robust optimization," European Journal of Operational Research, Elsevier, vol. 277(3), pages 799-813.
    2. Marcio Costa Santos & Michael Poss & Dritan Nace, 2018. "A perfect information lower bound for robust lot-sizing problems," Annals of Operations Research, Springer, vol. 271(2), pages 887-913, December.
    3. Ali Haddad-Sisakht & Sarah M. Ryan, 2018. "Conditions under which adjustability lowers the cost of a robust linear program," Annals of Operations Research, Springer, vol. 269(1), pages 185-204, October.


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