Journal article
A data-driven distributionally robust bound on the expected optimal value of uncertain mixed 0-1 linear programming
Computational management science, Vol.15(1), pp.111-134
01/2018
DOI: 10.1007/s10287-018-0298-9
Abstract
This paper studies the expected optimal value of a mixed 0-1 programming problem with uncertain objective coefficients following a joint distribution. We assume that the true distribution is not known exactly, but a set of independent samples can be observed. Using the Wasserstein metric, we construct an ambiguity set centered at the empirical distribution from the observed samples and containing the true distribution with a high statistical guarantee. The problem of interest is to investigate the bound on the expected optimal value over the Wasserstein ambiguity set. Under standard assumptions, we reformulate the problem into a copositive program, which naturally leads to a tractable semidefinite-based approximation. We compare our approach with a moment-based approach from the literature on three applications. Numerical results illustrate the effectiveness of our approach.
Details
- Title: Subtitle
- A data-driven distributionally robust bound on the expected optimal value of uncertain mixed 0-1 linear programming
- Creators
- Guanglin Xu - Department of Management Sciences University of Iowa Iowa City IA 52242-1994 USASamuel Burer - Department of Management Sciences University of Iowa Iowa City IA 52242-1994 USA
- Resource Type
- Journal article
- Publication Details
- Computational management science, Vol.15(1), pp.111-134
- Publisher
- Springer Berlin Heidelberg
- DOI
- 10.1007/s10287-018-0298-9
- ISSN
- 1619-697X
- eISSN
- 1619-6988
- Language
- English
- Date published
- 01/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083865402771
Metrics
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