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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 addresses a scheduling problem where patients with different priorities are scheduled for elective surgery in a surgical facility, which has a limited capacity, and formulated a stochastic dynamic programming model to address this problem.

126 citations

Journal ArticleDOI
TL;DR: In this article, a multistage stochastic programming model is proposed for the planning of offshore oil or gas field infrastructure under uncertainty, where the main uncertainties considered are in the initial maximum oil and gas flowrate, the recoverable oil volume, and the water breakthrough time of the reservoir, which are represented by discrete distributions.
Abstract: The planning of offshore oil or gas field infrastructure under uncertainty is addressed in this article. The main uncertainties considered are in the initial maximum oil or gas flowrate, the recoverable oil or gas volume, and the water breakthrough time of the reservoir, which are represented by discrete distributions. Furthermore, it is assumed that these uncertainties are not immediately realized, but are gradually revealed as a function of design and operation decisions. To account for these decision-dependent uncertainties, we propose a multistage stochastic programming model that captures the complex economic objectives and nonlinear reservoir behavior and simultaneously optimizes the investment and operating decisions over the entire planning horizon. The proposed solution algorithm relies on a duality-based branch-and-bound method involving subproblems as nonconvex mixed-integer nonlinear programs. Several examples involving nonlinear reservoir models are presented to illustrate the application of ...

126 citations

Journal ArticleDOI
TL;DR: This paper discusses three classes of dynamic optimization problems with discontinuities: path-constrained problems, hybrid discrete/continuous problems, and mixed-integer dynamic optimize problems.
Abstract: Many engineering tasks can be formulated as dynamic optimization or open-loop optimal control problems, where we search a priori for the input profiles to a dynamic system that optimize a given performance measure over a certain time period. Further, many systems of interest in the chemical processing industries experience significant discontinuities during transients of interest in process design and operation. This paper discusses three classes of dynamic optimization problems with discontinuities: path-constrained problems, hybrid discrete/continuous problems, and mixed-integer dynamic optimization problems. In particular, progress toward a general numerical technology for the solution of large-scale discontinuous dynamic optimization problems is discussed.

126 citations

01 May 1997
TL;DR: A Minimax Regret formulation suitable for large-scale linear programming models and experimentally verified that the minimax regret strategy depends only on the extremal scenarios and not on the intermediate ones, making the approach computationally efficient.
Abstract: Classical stochastic programming has already been used with large-scale LP models for long-term analysis of energy-environment systems. We propose a Minimax Regret formulation suitable for large-scale linear programming models. It has been experimentally verified that the minimax regret strategy depends only on the extremal scenarios and not on the intermediate ones, thus making the approach computationally efficient. Key results of minimax regret and minimum expected value strategies for Greenhouse Gas abatement in the Province of Quebec, are compared.

126 citations

Journal ArticleDOI
TL;DR: In this article, the authors apply dynamic programming and linear programming techniques to nonlinear and multiple-processor scheduling problems with deferral costs, where the number of jobs is fixed and the processing times for the jobs are equal.
Abstract: A class of scheduling problems involving deferral costs has been formulated by McNaughton, who has described a simple method of solution for the linear, single-processor case. In this report dynamic programming and linear programming techniques are applied to nonlinear and multiple-processor problems. A dynamic programming solution of the nonlinear, single processor problem is possible, provided the number of jobs is small. Transportation methods of linear programming can be used to solve large nonlinear, multiple-processor problems, provided the processing times for the jobs are equal. Approximate and/or partial solutions are possible for other cases.

126 citations


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