Results 221 to 230 of about 25,783 (262)

Discrete Stochastic Programming

Management Science, 1968
A method is presented for solving linear programming problems where (any number of) the functional, restraint, and input-output coefficients are subject to discrete; probability distributions. The objective function is formulated in terms of variance and/or expectation.
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The Value of Information and Stochastic Programming

Operations Research, 1970
The problem of planning under uncertainty has many aspects; in this paper we consider the aspect that has to do with evaluating the state of information. We address ourselves to the question of how much better (i.e., how much more profitable) we could expect our plans to be if somehow we could know at planning time what the outcomes of the uncertain ...
Mordecai Avriel, A. C. Williams
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Introduction to Stochastic Programming

Journal of the Operational Research Society, 1998
(1998). Introduction to Stochastic Programming. Journal of the Operational Research Society: Vol. 49, No. 8, pp. 897-898.
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Scenarios for Multistage Stochastic Programs

Annals of Operations Research, 2000
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Dupacova, Jitka, Consigli, Giorgio, Wallace, S W   +2 more
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A Stochastic Programming Model

Econometrica, 1963
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On a Class of Minimax Stochastic Programs

SIAM Journal on Optimization, 2004
Summary: For a particular class of minimax stochastic programming models, we show that the problem can be equivalently reformulated into a standard stochastic programming problem. This permits the direct use of standard decomposition and sampling methods developed for stochastic programming.
Alexander Shapiro 0001, Shabbir Ahmed 0001   +1 more
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Stochastic programming and stochastic control

Trabajos de Estadistica y de Investigacion Operativa, 1975
We consider the passive and active approach to stochastic linear programming and mention also some alternative approaches. Stochastic control theory is discussed in its discrete version. The theory is illustrated with the help of econometric models for Indian economic planning. 1. Stochastic programming. 2. Stochastic control theory.
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Stochastic Programs with Incomplete Information

Operations Research, 1976
This paper treats a simple recourse problem. We consider the problem of reducing the feasible set into a smaller efficient set when partial information about the random parameters is known. We analyze some examples and give applications to stochastic programs with compete information.
Aharon Ben-Tal, Eithan Hochman
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Optimality functions in stochastic programming

Mathematical Programming, 2011
A stochastic programming problem is considered with nonlinear and possibly non-convex expected value objective and constraint functions. The concept of an optimality function is extended to stochastic programs, and applied to evaluate the quality of a candidate solution by means of confidence intervals.
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Distributionally Robust Stochastic Programming

SIAM Journal on Optimization, 2017
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