An integer programming approach for linear programs with probabilistic constraints

By James R. Luedtke, Shabbir Ahmed, George L. Nemhauser. Mathematical programming, 2007

417 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/512c9f4301c06d0a69196d9d3783962e6c41e014

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In Control and Systems Engineering

In Industrial and Manufacturing Engineering

In Management Information Systems

Citations in the corpus

Mixed Integer Linear Programming Formulation Techniques (2015, background): “Examples of this include the formulations for joint probabilistic constraints introduced in [101, 114, 157].”
Data-driven chance constrained stochastic program (2015, methodology): “To accelerate the solution process, we implement the branch-and-cut algorithm with the strong cutting planes derived in [29] and [22] embedded.”
Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications (2009, background): “, Dentcheva, Prékopa and Ruszczyński [6], Luedtke, Ahmed and Nemhauser [7]).”
A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output (2012, methodology): “For Policy 3, we derive a strong formulation as studied in [19] to speed up the algorithm.”
Monte Carlo sampling-based methods for stochastic optimization (2014, background): “[157] provide a strengthened integer programming formulation for the problem when H(x, ξ) is of the form maxi{ξi − hi(x)}, i.”
A Sampling-and-Discarding Approach to Chance-Constrained Optimization: Feasibility and Optimality (2011, methodology): “All methods in [15, 21, 16, 17, 18, 19, 22, 20, 23] can be used and thus this paper leverages on these previous contributions.”
A Sample Approximation Approach for Optimization with Probabilistic Constraints (2008, background): “Choosing the optimal set is an NP -hard problem even in a very special case [20].”
Stochastic network design for disaster preparedness (2015, methodology): “…methods utilize the p-efficiency concept (Dentcheva et al., 2001; Lejeune and Ruszczyński, 2007; Lejeune and Noyan, 2010; Dentcheva and Martinez, 2012), valid inequalities to derive strengthened MIP reformulations (Ruszczyński, 2002; Luedtke et al., 2010), and conservative convex approximations.”