Add to ORKGThe field of combinatorial optimization under uncertainty has received increasing attention within the last years. Combinatorial optimization problems which contain uncertain and dynamic information in their formulation can be used to generate more realistic models of real world problems. Many relevant problems make use of stochastic information t
We present a new research domain in Operational Research, probabilistic combinatorial optimization p...
Decision making under uncertainty is an important topic in many Industries, such as telecommunicatio...
The traveling salesman problem is one of the most famous combinatorial optimization problems, and ha...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
We study the stochastic versions of a broad class of combinatorial problems where the weights of the...
The Orienteering Problem (OP) is a generalization of the well-known traveling salesman problem and h...
Incomplete information is a major challenge when translating combinatorial optimization results to r...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of c...
We present a new research domain in Operational Research, probabilistic combinatorial optimization p...
Decision making under uncertainty is an important topic in many Industries, such as telecommunicatio...
The traveling salesman problem is one of the most famous combinatorial optimization problems, and ha...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
In practical problem situations data are usually inherently unreliable. A mathematical representatio...
We study the stochastic versions of a broad class of combinatorial problems where the weights of the...
The Orienteering Problem (OP) is a generalization of the well-known traveling salesman problem and h...
Incomplete information is a major challenge when translating combinatorial optimization results to r...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of c...
We present a new research domain in Operational Research, probabilistic combinatorial optimization p...
Decision making under uncertainty is an important topic in many Industries, such as telecommunicatio...
The traveling salesman problem is one of the most famous combinatorial optimization problems, and ha...