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Adjustable robust optimization with decision rules based on inexact revealed data

Frans de Ruiter(fjctderuiter***at***gmail.com)
Aharon Ben-Tal(abental***at***ie.technion.ac.il)
Ruud Brekelmans(R.C.M.Brekelmans***at***uvt.nl)
Dick den Hertog(D.denHertog***at***uvt.nl)

Abstract: Adjustable robust optimization (ARO) is a technique to solve dynamic (multistage) optimization problems. In ARO, the decision in each stage is a function of the information accumulated from the previous periods on the values of the uncertain parameters. This information, however, is often inaccurate; there is much evidence in the information management literature that even in our Big Data era the data quality is often poor. Reliance on the data “as is” may then lead to poor performance of ARO, or in fact to any “data-driven” method. In this paper, we remedy this weakness of ARO by introducing a methodology that treats past data itself as an uncertain parameter. We show that algorithmic tractability of the robust counterparts associated with this extension of ARO is still maintained. The benefit of the new approach is demonstrated by a production-inventory application.

Keywords: adjustable robust optimization, decision rules, inexact data, poor data quality.

Category 1: Robust Optimization

Citation: CentER Discussion Paper CDP 2014-003, January 2014, CentER, Department of Econometrics and Operations Research, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands

Download: [PDF]

Entry Submitted: 01/09/2014
Entry Accepted: 01/09/2014
Entry Last Modified: 01/09/2014

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