Abstract
Based on the MPS standard for linear programs, data conventions for the description of multistage stochastic linear programs were described by Birge et al. [3]. This paper proposes extensions to the so-called SMPS standard, in order to address known shortcomings and to extend the range of problems that can be expressed within the standard.
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References
E.M.L. Beale, On minimizing a convex function subject to linear inequalities, Journal of the Royal Statistical Society, Series B 17 (1955) 173–184.
J.R. Birge, Decomposition and partitioning methods for multistage stochastic linear programs, Operations Research 33 (1985) 989–1007.
J.R. Birge, M.A.H. Dempster, H.I. Gassmann, E.A. Gunn, A.J. King and S.W. Wallace, A standard input format for multiperiod stochastic linear programs, COAL Newsletter #17 (1987) 1-19.
A. Charnes and W.W. Cooper, Chance-constrained programming, Management Science 5 (1959) 73–79.
G.B. Dantzig, Linear programming under uncertainty, Management Science 1 (1955) 197–206.
J. Edwards, J. Birge, A. King and L. Nazareth, A standard input format for computer codes which solve stochastic programs with recourse and a library of utilities to simplify its use, Working Paper WP-85-03, International Institute for Applied Systems Analysis, Laxenburg, Austria (1985).
Yu. Ermoliev and R.J-B. Wets, Numerical Methods for Stochastic Programming (Springer, 1987).
H.I. Gassmann and A.M. Ireland, On the formulation of stochastic linear programs using algebraic modelling languages, Annals of Operations Research 64 (1996) 83–112.
International Business Machines, Inc., Mathematical Programming Subsystem-Extended (MPSX) and Generalized Upper Bounding (GUB) Program Description, Document number SH20-0968-1 (1972).
International Business Machines, Inc., Mathematical Programming Subsystem-Extended (MPSX) and Mixed Integer Programming (MIP) Program Description, Document number GH19-1091-0.
International Business Machines, Inc., Passing your model to OSL using Mathematical Programming System (MPS) format, WWW document, http://www.software.ibm.com/sos/features/featur11.htm.
N.L. Johnson and S. Kotz, Distributions in Statistics, Vol. 4 (Wiley, New York, 1972).
P. Kall and J. Mayer, Private communication (4 September 1995).
P. Kall and S.W. Wallace, Stochastic Programming (Wiley, New York, 1994).
A.J. King, An implementation of the Lagrangian finite-generation method, in: Numerical Techniques for Stochastic Optimization, eds. Yu. Ermoliev and R.J.-B. Wets, Springer Series in Computational Mathematics, Vol. 10 (Springer, Berlin, 1988).
D. Klingman, A. Napier and J. Stutz, NETGEN: A program for generating large scale capacitated assignment, transportation, and minimum cost flow network problems, Management Science 20 (1974) 814–821.
J.M. Mulvey, R.J. Vanderbei and S.A. Zenios, Robust optimization of large-scale systems, Operations Research 43 (1993) 264–281.
J.M. Mulvey and H. Vladimirou, Stochastic network optimization models for investment planning, Annals of Operations Research 31 (1989) 399–424.
S.W.Wallace, Solving stochastic programs with network recourse, Networks 16 (1986) 295–317.
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Gassmann, H., Schweitzer, E. A Comprehensive Input Format for Stochastic Linear Programs. Annals of Operations Research 104, 89–125 (2001). https://doi.org/10.1023/A:1013138919445
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DOI: https://doi.org/10.1023/A:1013138919445