AbstractWe define and study two versions of the bipartite matching problem in the framework of two-stage stochastic optimization with recourse. In one version, the uncertainty is in the second stage costs of the edges, and in the other version, the uncertainty is in the set of vertices that needs to be matched. We prove lower bounds, and analyze efficient strategies for both cases. These problems model real-life stochastic integral planning problems, such as commodity trading, reservation systems and scheduling under uncertainty
Stochastic optimization problems attempt to model uncertainty in the data by assuming that the input...
This article deals with the two-stage stochastic model, which aims at explicitly taking into account...
Uncertainty exists everywhere; from the future price of a stock, to the routing time of a network pa...
AbstractWe define and study two versions of the bipartite matching problem in the framework of two-s...
We define and study two versions of the bipartite matching problem in the framework of two-stage sto...
We study two-stage, finite-scenario stochastic versions of several combinatorial optimization proble...
Uncertainty is a facet of many decision environments and might arise for various reasons, such as un...
We consider the online bipartite matching problem within the context of stochastic probing with comm...
Abstract We introduce the two-stage stochastic maximum-weight matching problem and demonstrate that ...
Abstract. We consider the following stochastic optimization problem first intro-duced by Chen et al....
Stochastic optimization problems attempt to model uncertainty in the data by assuming that (part of)...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
We initiate the design of approximation algorithms for stochastic combinatorial optimization problem...
Thesis (Ph.D.)--University of Washington, 2018We study stochastic combinatorial optimization models ...
Abstract. The field of stochastic optimization studies decision making under uncertainty, when only ...
Stochastic optimization problems attempt to model uncertainty in the data by assuming that the input...
This article deals with the two-stage stochastic model, which aims at explicitly taking into account...
Uncertainty exists everywhere; from the future price of a stock, to the routing time of a network pa...
AbstractWe define and study two versions of the bipartite matching problem in the framework of two-s...
We define and study two versions of the bipartite matching problem in the framework of two-stage sto...
We study two-stage, finite-scenario stochastic versions of several combinatorial optimization proble...
Uncertainty is a facet of many decision environments and might arise for various reasons, such as un...
We consider the online bipartite matching problem within the context of stochastic probing with comm...
Abstract We introduce the two-stage stochastic maximum-weight matching problem and demonstrate that ...
Abstract. We consider the following stochastic optimization problem first intro-duced by Chen et al....
Stochastic optimization problems attempt to model uncertainty in the data by assuming that (part of)...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
We initiate the design of approximation algorithms for stochastic combinatorial optimization problem...
Thesis (Ph.D.)--University of Washington, 2018We study stochastic combinatorial optimization models ...
Abstract. The field of stochastic optimization studies decision making under uncertainty, when only ...
Stochastic optimization problems attempt to model uncertainty in the data by assuming that the input...
This article deals with the two-stage stochastic model, which aims at explicitly taking into account...
Uncertainty exists everywhere; from the future price of a stock, to the routing time of a network pa...