Abstract
Many practical decisions have to be made while future data are uncertain. The stochastic programming approach to such decision problems is to model the uncertain data as random parameters and to assume that all probabilistic information concerning these random parameters is known or can be accurately estimated. A particular class of such models, studied in this thesis, comprises mixedinteger recourse models. These models have a wide range of applications in e.g. engineering, logistics, energy, and finance. They combine the modeling power but also the difficulties of random parameters and integer decision variables, so that in general they are extremely difficult to solve.
This thesis contributes to the theory of mixedinteger recourse models by constructing approximations having desirable properties (such as convexity) for optimization purposes. To guarantee the performance of these approximations, error bounds on the approximation error are derived. Several subclasses and problem instances of mixedinteger recourse models are considered, ranging from simple integer recourse models to mixedinteger recourse models in general.
This thesis contributes to the theory of mixedinteger recourse models by constructing approximations having desirable properties (such as convexity) for optimization purposes. To guarantee the performance of these approximations, error bounds on the approximation error are derived. Several subclasses and problem instances of mixedinteger recourse models are considered, ranging from simple integer recourse models to mixedinteger recourse models in general.
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Award date  7Sep2015 
Place of Publication  [Groningen] 
Publisher  
Print ISBNs  9789036778930 
Electronic ISBNs  9789036778923 
Publication status  Published  2015 