On deviation measures in stochastic integer programming

Stochastic Integer Programming Bibliography

Stochastic Integer Programming

1. Books and Surveys 2. Theory 2.1 Structural properties of the EVF 2.2 Complexity related results 3. General Purpose Algorithms 3.1 Benders’ decomposition methods 3.2 Gröbner basis methods 3.3 Convex approximations of SIR models 3.4 Other methods 4. Solution of Specific Problems 4.1 Production planning and scheduling problems 4.2 Network and routing problems 4.3 Location problems 4.4 Other pro...

Approximation in Stochastic Integer Programming

Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solutions. However, efficiency in the complexity theoretical sense is usually not taken into account. Quality statements mostly rem...

On Stochastic Integer Programming under Probabilistic Constraints

We consider stochastic programming problems with probabilistic constraints in volving integer valued random variables The concept of p e cient points of a prob ability distribution is used to derive various equivalent problem formulations Next we modify the concept of r concave discrete probability distributions and analyse its relevance for problems under consideration These notions are used t...

Dual Decomposition in Stochastic Integer Programming Dual decomposition in stochastic integer programming

We present an algorithm for solving stochastic integer programming problems with recourse, based on a dual decomposition scheme and Lagrangian relaxation. The approach can be applied to multi-stage problems with mixed-integer variables in each time stage. Numerical experience is presented for some two-stage test problems.