List of notations Preface to the second edition Preface to the first edition 1. Stochastic programming models 2. Two-stage problems 3. Multistage problems 4. Optimization models with probabilistic constraints 5. Statistical inference 6. Risk averse optimization 7. Background material 8. Bibliographical remarks Bibliography Index.

Lectures on Stochastic Programming: Modeling and Theory

We review and develop different tractable approximations to individual chance-constrained problems in robust optimization on a variety of uncertainty sets and show their interesting connections with bounds on the conditional-value-at-risk (CVaR) measure. We extend the idea to joint chance-constrained problems and provide a new formulation that improves upon the standard approach. Our approach builds on a classical worst-case bound for order statistics problems and is applicable even if the constraints are correlated. We provide an application of the model on a network resource allocation problem with uncertain demand.

/pdf/from-cvar-to-uncertainty-set-implications-in-joint-chance-1m0i09rnxx.pdf

From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization

In this paper we consider stochastic programming problems where the objective function is given as an expected value function. We discuss Monte Carlo simulation based approaches to a numerical solution of such problems. In particular, we discuss in detail and present numerical results for two-stage stochastic programming with recourse where the random data have a continuous (multivariate normal) distribution. We think that the novelty of the numerical approach developed in this paper is twofold. First, various variance reduction techniques are applied in order to enhance the rate of convergence. Successful application of those techniques is what makes the whole approach numerically feasible. Second, a statistical inference is developed and applied to estimation of the error, validation of optimality of a calculated solution and statistically based stopping criteria for an iterative alogrithm. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.

/pdf/a-simulation-based-approach-to-two-stage-stochastic-1t21a8yegp.pdf

A simulation-based approach to two-stage stochastic programming with recourse

The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations

To each stochastic program corresponds an equivalent deterministic program. The purpose of this paper is to compile and extend the known properties for the equivalent deterministic program of a sto...

Stochastic Programs with Fixed Recourse: The Equivalent Deterministic Program

A linear programming problem is said to be stochastic if one or more of the coefficients in the objective function or the system of constraints or resource availabilities is known only by its probability distribution. Various approaches are available in this case, which may be classified into three broad types: ‘chance constrained programming’, ‘two-stage programming under uncertainty’ and ‘stochastic linear programming’. For problems of ‘stochastic linear programming’ a distinction is usually made between two related approaches to stochastic programming, the passive and the active approach respectively. In the passive approach to stochastic linear programming the statistical distribution of the optimum value of the objective function is estimated either exactly or approximately by numerical methods and optimum decision rules are based on the different characteristics of the estimated distribution. In the active approach, a new set of decision variables are introduced which indicate the proportions of dif...

On Some Theorems of Stochastic Linear Programming with Applications

A linear programming problem is said to be stochastic if one or more of the coefficients in the objective function or the system of constraints or resource availabilities is known only by its probability distribution. A distinction is usually made between two related approaches to stochastic linear programming, the active and passive approach respectively. An extension of the duality theorem of non-stochastic or deterministic programming problem has been attempted in this paper in the area of stochastic linear programming in its two approaches. The method of proof is based on the idea that since the parameter space defined by a stochastic linear programme is the topological product of the real line with itself, it forms a first countable topological space. Using a set of distinct and selected points in the parameter space the concepts of feasibility, optimality and duality are extended to stochastic linear programming problems of arbitrary dimensionality. Based on the non-singular regions of the parameter space of a stochastic linear programming problem the theorem utilizes the conditions of convergence of the sequence of distinct and selected points in the parameter space to a limit point and thereby generalizes the duality theorem in the stochastic case. Furthermore it is shown that the regions of feasibility of the active and passive approaches of stochastic linear programming may be different, so that on this basis it may be possible to establish some inequality relations for the optimal solutions defined for the respective feasible regions.

A weak duality theorem for stochastic linear programming

On the stability of solutions under error in stochastic linear programming

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=3103&context=rtd

Stochastic linear programming

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On Some Theorems of Stochastic Linear Programming with Applications

A weak duality theorem for stochastic linear programming

On the stability of solutions under error in stochastic linear programming

Stochastic linear programming