dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Hoogeveen, Dr. J. A. | |
dc.contributor.advisor | van den Akker, Dr. Ir. J. M. | |
dc.contributor.author | Komen, A. | |
dc.date.accessioned | 2017-06-23T17:05:32Z | |
dc.date.available | 2017-06-23T17:05:32Z | |
dc.date.issued | 2017 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/25971 | |
dc.description.abstract | In this thesis an attempt is made to find out how the type of uncertainty (discrete
and finite or polyhedral) in
uences performance of Benders' decomposition
[4] and Column & Constraint Generation [24] when solving the demand robust
location-transportation problem. A generalization of Benders' decomposition is
presented to make it applicable to a large group of demand robust optimization
problems. Also, Column & Constraint Generation is adapted to be used
on discrete and finite uncertainty sets. In [24] it was shown that Column &
Constraint Generation is able to solve the problem a lot better than a standard
implementation of Benders' decomposition. The performance comparison for
discrete and finite uncertainty sets made in this thesis is new. On top of that,
a number of techniques for making Benders' faster are applied. Special attention
is paid to the role of the MIP-solver that is used as a black box for both
algorithms. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 530033 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Benders' Decomposition vs.Column & Constraint Generation, a Closer Look | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Demand Robust Optimization; Benders' Decomposition; Column & Constraint Generation | |
dc.subject.courseuu | Computing Science | |