Abstract
We propose a new variant of the two-stage recourse model. It can be used e.g., in managing resources in whose supply random interruptions may occur. Oil and natural gas are examples for such resources. Constraints in the resulting stochastic programming problems can be regarded as generalizations of integrated chance constraints. For the solution of such problems, we propose a new decomposition method that integrates a bundle-type convex programming method with the classic distribution approximation schemes. Feasibility and optimality issues are taken into consideration simultaneously, since we use a convex programming method suited for constrained optimization. This approach can also be applied to traditional two-stage problems whose recourse functions can be extended to the whole space in a computationally efficient way. Network recourse problems are an example for such problems. We report encouraging test results with the new method.
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References
Andersen ED, Andersen KD (2000) The MOSEK interior-point optimizer for linear programming: an implementation of the homogeneous algorithm. In: Frenk H, Roos K, Terlaky T, Zhang S, (eds) High performance optimization. Kluwer, Dordrecht, pp 197–232
Beale EML (1955) On minimizing a convex function subject to linear inequalities. J R Stat Soc Ser B 17:173–184
Benders JF (1962) Partitioning procedures for solving mixed-variables programming problems. Numer Math 4:238–252 republished in Comput Manage Sci 2:3–19 (2005)
Birge JR, Louveaux FV (1997) Introduction to Stochastic Programming. Springer, Berlin Heidelberg New York
Birge JR, Wets RJ-B (1986) Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse. In: Prékopa A, Wets RJ-B (eds) Stochastic Programming 84, Vol 1. Mathematical Programming Study 27:54–102
Björck Å (1996) Numerical methods for least squares problems. Society for Industrial and Applied Mathematics, Philadelphia
Dantzig GB (1955) Linear programming under uncertainty. Manage Sci 1:197–206
Dantzig GB, Madansky A (1961) On the solution of two-stage linear programs under uncertainty. In: Proceedings of the 4th Berkeley symposium on mathematical statistics and probability, vol 1: pp 165–176. University of California Press, Berkeley
Dantzig GB, Wolfe P (1960) The decomposition principle for linear programs. Oper Res 8:101–111
Fábián CI (2000) Csendes T, Rapcsák T (eds) Bundle-type methods for inexact data. Central Eur J Oper Res 8 (special issue); 35–55
Fábián CI (2005) Decomposing CVaR minimization in two–stage stochastic models. Stochastic Programming E-Print Series 20
Fábián CI (2006) Handling CVaR objectives and constraints in two-stage stochastic models. RUTCOR Research Report, vol 5
Frauendorfer K (1988) Solving SLP recourse problems with arbitrary multivariate distributions – the dependent case. Math Oper Res 13:377–394
Frauendorfer K, Kall P (1988) A solution method for SLP recourse problems with arbitrary distributions – The independent case. Probl Control Inf Theory 17:177–205
Gassmann HI, Wallace SW (1996) Solving linear programs with multiple right–hand sides: pricing and ordering schemes. Ann Oper Res 64:237–259
Gondzio J, Grothey A (2003) Reoptimization with the primal-dual interior point method. SIAM J Optim 13:842–864
Kall P (1980) Solving complete fixed recourse problems by successive discretization. In: Kall P, Prékopa A (eds) Recent Results in Stochastic Programming, Lecture Notes in Economics and Math. Systems 170. Springer, Berlin, Heidelberg New York, pp 135–138
Kall P, Mayer J (2005) Stochastic Linear Programming: Models, Theory, and Computation. International series in operations research and management science. Springer, Berlin, Heidelberg New York
Kall P, Stoyan D (1982) Solving stochastic programming problems with recourse including error bounds. Math Opernforsch Stat Ser Optim 13:431–447
Kall P, Wallace SW (1994) Stochastic programming. Wiley, Chichester
Kennington JL, Helgason RV (1980) Algorithms for Network Programming. Wiley, New York
Klafszky E, Terlaky T (1992) On the ellipsoid method. Radov Mat 8:269–280
Klein Haneveld WK (1986) Duality in stochastic linear and dynamic programming. Lecture Notes in Economics and Mathematical Systems, vol 274. Springer, Berlin Heidelberg New York
Künzi-Bay A, Mayer J (2006) Computational aspects of minimizing conditional value-at-risk. Comput Manage Sci 3:3–27
Lemaréchal C (1982) Basic theory in nondifferentiable optimization. Research Report No. 181, Institut National de Recherche en Informatique at en Automatique, Domaine de Voluceau, Rocquencourt, France
Lemaréchal C, Nemirovskii A, Nesterov Yu (1995) New variants of bundle methods. Math Program 69:111–147
Linderoth JT, Shapiro A, Wright SJ (2002) The empirical behavior of sampling methods for stochastic programming. Optimization Technical Report 02-01. Computer Science Department, University of Wisconsin-Madison
Mak W-K, Morton D, Wood RK (1999) Monte Carlo bounding techniques for determining solution quality in stochastic programs. Oper Res Lett 24:47–56
Maros I (2003a) A generalized dual phase-2 simplex algorithm. Eur J Oper Res 149:1–16
Maros I (2003b) Computational techniques of the simplex method. Kluwer, Boston
Mayer J (1998) Stochastic linear programming algorithms. Gordon and Breach, Amsterdam
Mulvey JM, Ruszczyński A (1995) A new scenario decomposition method for large scale stochastic optimization. Oper Res 43:477–490
Norkin VI, Pflug GCh, Ruszczyński A (1998) A branch and bound method for stochastic global optimization. Math program 83:425–450
Prékopa A (1971) Logarithmic concave measures with applications to stochastic programming. Acta Sci Math (Szeged) 32:301–316
Prékopa A (1973) Contributions to the theory of stochastic programming. Math Program 4:202–221
Prékopa A (1995). Stochastic Programming. Kluwer, Dordrecht
Prékopa A (2003) Probabilistic programming. In: Ruszczyński A, Shapiro A (eds) Stochastic Programming, Handbooks in Operations Research and Management Science vol 10, pp 267–351 Elsevier, Amsterdam
Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2:21–41
Roos C, Terlaky T, Vial J-Ph (1997) Theory and Algorithms for Linear optimization. Wiley, Chichester
Ruszczyński A (1986) A regularized decomposition method for minimizing the sum of polyhedral functions. Math Program 35:309–333
Ruszczyński A (2003) Decomposition methods. In: Ruszczyński A, Shapiro A (eds) Stochastic Programming. Handbooks in Operations Research and Management Science, vol 10, pp 141-211 Elsevier, Amsterdam
Ruszczyński A, Shapiro A (2003) Stochastic Programming Models. In: Ruszczyński A, Shapiro A (eds)Stochastic Programming. Handbooks in Operations Research and Management Science, vol 10, pp 1-64 Elsevier, Amsterdam
Ruszczyński A, Świetanowski A (1997) Accelerating the regularized decomposition method for two-stage stochastic linear problems. Eur J Oper Res 101:328–342
Schrijver A (1986) Theory of linear and integer programming. Wiley, Chichester
Shapiro A (2003) Monte Carlo sampling methods. In: Ruszczyński A, Shapiro A (eds) Stochastic Programming, Handbooks in Operations Research and Management Science, vol 10 pp 353–425 Elsevier, Amsterdam
Shapiro A, Homem-de-Mello T (1998) A simulation-based approach to two-stage stochastic programming with recourse. Math Program 81:301–325
Shapiro A, Homem-de-Mello T (2000) On the rate of convergence of Monte Carlo approximations of stochastic programs. SIAM J Optim 11:70–86
Szántai T (1988) A computer code for the solution of probabilistic-constrained stochastic programming problems. In: Ermoliev Yu, Wets RJ-B (eds) Numerical Techniques for Stochastic Optimization. Springer, Berlin Heidelberg New York
Van Slyke R, Wets RJ-B (1969) L-Shaped linear programs with applications to optimal control and stochastic programming. SIAM J Appl Math 17:638–663
Wets RJ-B (1974) Stochastic programs with fixed recourse: the equivalent deterministic program. SIAM Review 16:309–339
Zakeri G, Philpott AB, Ryan DM (2000) Inexact cuts in Benders decomposition. SIAM J Optim 10:643–657
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Fábián, C.I., Szőke, Z. Solving two-stage stochastic programming problems with level decomposition. CMS 4, 313–353 (2007). https://doi.org/10.1007/s10287-006-0026-8
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DOI: https://doi.org/10.1007/s10287-006-0026-8