Scalable Heuristics for a Class of Chance-Constrained Stochastic Programs

References

• Ahmed S., Shapiro A., Chen Z.-L., Raghavan S. Solving chance-constrained stochastic programs via sampling and integer programming. Tutorials in Operations Research (2008) (INFORMS, Hanover, MD) 261–269Link, Google Scholar
• Birge J. R., Louveaux F.Introduction to Stochastic Programming (1997) (Springer, New York) Google Scholar
• Ermoliev Y. M, Norkin V. I., Wets R. J-B. The minimization of semicontinuous functions: Mollifier subgradients. SIAM J. Control Optim. (1995) 33(1):149–167Crossref, Google Scholar
• Holton G. A.Value-at-Risk: Theory and Practice (2003) (Academic Press, San Diego) Google Scholar
• ILOG IBM ILOG CPLEX: High-performance mathematical programming engine. (2007) . Retrieved March 2, 2010, http://www.ilog.com/products/cplexGoogle Scholar
• Infanger G. Monte Carlo (importance) sampling within a Benders decomposition algorithm for stochastic linear programs. Ann. Oper. Res. (1993) 39(1–4):69–95Google Scholar
• Kall P., Wallace S. W.Stochastic Programming (1994) (John Wiley & Sons, Chichester, UK) Google Scholar
• Luedtke J., Ahmed S. A sample approximation approach for optimization with probabilistic constraints. SIAM J. Optim. (2008) 19(2):674–699Crossref, Google Scholar
• Lulli G., Sen S. A branch-and-price algorithm for multistage stochastic integer programming with application to stochastic batch-sizing problems. Management Sci. (2004) 50(6):786–796Link, Google Scholar
• Nemirovski A., Shapiro A. Convex approximations of chance-constrained programs. SIAM J. Optim. (2006) 17(4):969–996Crossref, Google Scholar
• Prekopa A., Ruszczyński A., Shapiro A. Probabilistic programming. Handbooks in Operations Research and Management Science: Stochastic Programming (2003) 10(Elsevier, Amsterdam) 267–352Crossref, Google Scholar
• Rockafellar R. T., Wets R. J-B. Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. (1991) 16(1):119–147Link, Google Scholar
• Ruszczyński A. Probabilistic programming with discrete distributions and precedence constrained knapsack polyhedra. Math. Programming (2002) 93(2):195–215Crossref, Google Scholar
• Salinetti G. Approximations for chance-constrained programming problems. Stochastics (1983) 10(3–4):157–179Crossref, Google Scholar
• Savage E. L., Schruben L. W., Yucesan E. On the generality of event-graph models. INFORMS J. Comput. (2005) 17(1):3–9Link, Google Scholar
• Watson J.-P., Woodruff D. L., Strip D. R. Progressive hedging innovations for a stochastic spare parts support enterprise problem. (2007) . Technical Report SAND-2007-3722J, Sandia National Laboratories, Albuquerque, NMGoogle Scholar