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

Frans de Ruiter, A. Ben-Tal, Ruud Brekelmans, Dick den Hertog

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

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.
Original languageEnglish
Pages (from-to)45-66
JournalComputational Management Science
Volume14
Issue number1
Early online dateApr 2016
DOIs
Publication statusPublished - Jan 2017

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Information management
Robust optimization
Decision rules
Inventory systems
Planning
Production-inventory
Remedies
Uncertainty
Uncertain demand
Optimization model
Data quality
Production capacity

Keywords

  • adjustable robust optimization
  • production-inventory problems
  • decision rules
  • inexact data
  • poor data quality

Cite this

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title = "Robust optimization of uncertain multistage inventory systems with inexact data in decision rules",
abstract = "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.",
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Robust optimization of uncertain multistage inventory systems with inexact data in decision rules. / de Ruiter, Frans; Ben-Tal, A.; Brekelmans, Ruud; den Hertog, Dick.

In: Computational Management Science, Vol. 14, No. 1, 01.2017, p. 45-66.

Research output: Contribution to journalArticleScientificpeer-review

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