A large number of problems in production planning and scheduling, location, transportation,
finance, and engineering design require that decisions be made in the presence of …

Probabilistic programming means two strongly connected models as well as the study of
their mathematical properties, solutions of the relevant optimization problems and their …

This book is about the interplay of computational commutative algebra and the theory of
convex polytopes. It centres around a special class of ideals in a polynomial ring: the class …

This paper develops a solution strategy for two-stage stochastic programs with integer
recourse. The proposed methodology relies on approximating the underlying stochastic …

We consider stochastic programming problems with probabilistic constraints involving
integer-valued random variables. The concept of ap-efficient point of a probability …

In the “standard” formulation of a stochastic program with recourse, the distribution ofthe
random parameters is independent of the decisions. When this is not the case, the problemis …

Stochastic integer programming: Page 1 Annals of Operations Research 85(1999)39757 39
Stochastic integer programming: General models and algorithms Willem K. Klein Haneveld and …

It is undeniable that geometric ideas have been very important to the foundations of modern
discrete optimization. The influence that geometric algorithms have in optimization was …

In this paper we present a framework for solving stochastic programs with complete integer
recourse and discretely distributed right-hand side vector, using Gröbner basis methods …

We study the problem of minimizing c· x subject to A· x= b. x≥ 0 and x integral, for a fixed
matrix A. Two cost functions c and c′ are considered equivalent if they give the same …