Paper Info
Title
An integer programming approach for linear programs with probabilistic constraints
Abstract
Linear programs with joint probabilistic constraints (PCLP) are difficult to solve because the feasible region is not convex. We consider a special case of PCLP in which only the right-hand side is random and this random vector has a finite distribution. We give a mixed-integer programming formulation for this special case and study the relaxation corresponding to a single row of the probabilistic constraint. We obtain two strengthened formulations. As a byproduct of this analysis, we obtain new results for the previously studied mixing set, subject to an additional knapsack inequality. We present computational results which indicate that by using our strengthened formulations, instances that are considerably larger than have been considered before can be solved to optimality.
Year
DOI
Venue
2010
10.1007/s10107-008-0247-4
Mathematical Programming: Series A and B
Keywords
Field
DocType
computational result,finite distribution,strengthened formulation,special case,linear program,probabilistic constraint,stochastic programming · integer programming · probabilistic constraints · chance constraints · mixing set,joint probabilistic constraint,feasible region,integer programming approach,additional knapsack inequality,random vector,stochastic programming
Mathematical optimization,Combinatorial optimization,Feasible region,Integer programming,Multivariate random variable,Linear programming,Knapsack problem,Probabilistic logic,Stochastic programming,Mathematics
Journal
Volume
Issue
ISSN
122
2
1436-4646
Citations 
PageRank 
References 
101
5.21
21
Authors
3
Search Limit
100101
Name
Order
Citations
PageRank
James Luedtke143925.95
Shabbir Ahmed21496104.25
George L. Nemhauser33035354.58