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Adjustable Robust Optimization with Decision Rules Based on Inexact Revealed DataF.J.C.T. de RuiterTilburg University - Center for Economic Research (CentER) Aharon Ben-TalTechnion-Israel Institute of Technology R.C.M. BrekelmansTilburg University - Center and Faculty of Economics and Business Administration Dick den HertogTilburg University - Department of Econometrics & Operations Research January 9, 2014 CentER Discussion Paper Series No. 2014-003 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.
Number of Pages in PDF File: 31 Keywords: adjustable robust optimization, decision rules, inexact data, poor data quality JEL Classification: C00, C15, C44, C61, C63 Date posted: January 10, 2014Suggested CitationContact Information
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