Paper Info
Title
An integer programming and decomposition approach to general chance-constrained mathematical programs
Abstract
We present a new approach for exactly solving general chance constrained mathematical programs having discrete distributions. Such problems have been notoriously difficult to solve due to nonconvexity of the feasible region, and currently available methods are only able to find provably good solutions in certain very special cases. Our approach uses both decomposition, to enable processing subproblems corresponding to one possible outcome at a time, and integer programming techniques, to combine the results of these subproblems to yield strong valid inequalities. Computational results on a chance-constrained two-stage problem arising in call center staffing indicate the approach works significantly better than both an existing mixed-integer programming formulation and a simple decomposition approach that does not use strong valid inequalities. Thus, the strength of this approach results from the successful merger of stochastic programming decomposition techniques with integer programming techniques for finding strong valid inequalities.
Year
DOI
Venue
2010
10.1007/978-3-642-13036-6_21
IPCO
Keywords
Field
DocType
existing mixed-integer programming formulation,approach result,general chance-constrained mathematical program,strong valid inequality,stochastic programming decomposition technique,simple decomposition approach,available method,new approach,chance-constrained two-stage problem,call center staffing,integer programming technique,discrete distribution,stochastic programming,mathematical programming
Discrete mathematics,Mathematical optimization,Computer science,Branch and price,Constraint programming,Integer programming,Feasible region,Reactive programming,Stochastic programming
Conference
Volume
ISSN
ISBN
6080
0302-9743
3-642-13035-6
Citations 
PageRank 
References 
15
0.69
16
Authors
1
Name
Order
Citations
PageRank
James Luedtke143925.95