An integer programming approach for linear programs with probabilistic constraints

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 formulati...

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Veröffentlicht in:	Mathematical programming Jg. 122; H. 2; S. 247 - 272
Hauptverfasser:	Luedtke, James, Ahmed, Shabbir, Nemhauser, George L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer-Verlag 01.04.2010 Springer Springer Nature B.V
Schlagworte:	Applied sciences Approximation Calculus of Variations and Optimal Control; Optimization Combinatorics Exact sciences and technology Flows in networks. Combinatorial problems Full Length Paper Hierarchies Inequality Integer programming Knapsack problem Linear programming Mathematical and Computational Physics Mathematical Methods in Physics Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Operational research and scientific management Operational research. Management science Probability Production planning Random variables Stochastic models Studies Systems engineering Theoretical Integer programming Probabilistic constraints 90C11 Chance constraints 90C15 Mixing set Stochastic programming Probabilistic approach Linear programming Mixed integer programming Random vector Combinatorial optimization Modeling Knapsack problem Case study Mathematical programming
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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