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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper considers the vehicle routing problem with stochastic demands, and a new solution framework for the problem using Markovian decision processes is presented.
Abstract: This paper considers the vehicle routing problem with stochastic demands. The objective is to provide an overview of this problem, and to examine a variety of solution methodologies. The concepts and the main issues are reviewed along with some properties of optimal solutions. The existing stochastic mathematical programming formulations are presented and compared and a new formulation is proposed. A new solution framework for the problem using Markovian decision processes is then presented.

290 citations

Book ChapterDOI
TL;DR: Recoverable robustness combines the flexibility of stochastic programming with the tractability and performances guarantee of the classical robust approach and is exemplified in delay resistant, periodic and aperiodic timetabling problems, and train platforming.
Abstract: We present a new concept for optimization under uncertainty: recoverable robustness A solution is recovery robust if it can be recovered by limited means in all likely scenarios Specializing the general concept to linear programming we can show that recoverable robustness combines the flexibility of stochastic programming with the tractability and performances guarantee of the classical robust approach We exemplify recoverable robustness in delay resistant, periodic and aperiodic timetabling problems, and train platforming

289 citations

01 Jan 1998
TL;DR: A COMPREHENSIVE approach called the decision support problem technique is being developed and implemented at the University of Houston to provide support for human judgment in designing an artifact that can be manufactured and maintained.
Abstract: 1. Our Frame of Reference A COMPREHENSIVE approach called the decision support problem technique" is being developed and implemented at the University of Houston to provide support for human judgment in designing an artifact that can be manufactured and maintained. Decision support problems (DSPs) provide a means for modeling decisions encountered in design, manufacture, and maintenance. Multiple objectives that are quantified using analysis-based "hard" and insight-based "soft" information can be modeled in the DSPs. For real-world, practical systems, not all of the information will be available for modeling systems comprehensively and correctly in the early stages of the project. Therefore, the solution to the problem, even if it is obtained using optimization techniques, cannot be the optimum with respect to the real world. However, this solution can be used to support a designer's quest for a superior solution. In a computerassisted environment, this support is provided in the form of optimal solutions for decision support problems. Formulation and solution of DSPs provide a means for making the following types of decisions:

289 citations

Journal ArticleDOI
TL;DR: In this paper, 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 for two-stage stochastic programming with recourse where the random data have a continuous distribution.
Abstract: 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.

287 citations

Journal ArticleDOI
TL;DR: In this paper, the authors focus on robustness of model-predictive control with respect to satisfaction of process output constraints and propose a method of improving such robustness by formulating output constraints as chance constraints.
Abstract: This work focuses on robustness of model-predictive control with respect to satisfaction of process output constraints. A method of improving such robustness is presented. The method relies on formulating output constraints as chance constraints using the uncertainty description of the process model. The resulting on-line optimization problem is convex. The proposed approach is illustrated through a simulation case study on a high-purity distillation column. Suggestions for further improvements are made.

286 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532