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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: In this paper, a mixed-integer stochastic programming approach is proposed to solve the problem of transmission line expansion and transmission power system expansion with consideration of system reliability and cost trade off.
Abstract: This paper proposes a mixed-integer stochastic programming approach to the solution of generation and transmission line expansion planning problem including consideration of system reliability. Favorable system reliability and cost trade off is achieved by the optimal solution. The problem is formulated as a two-stage recourse model where random uncertainties in area generation, transmission lines, and area loads are considered. Reliability index used in this problem is expected cost of load loss as this index incorporates duration and magnitude of load loss. The objective is to minimize the expansion cost in the first stage and the operation and expected cost of load loss in the second stage. Due to exponentially large number of system states (scenarios) in large power systems, direct application of the L-shaped algorithm seems impractical. The expected cost of load loss is therefore approximated by considering only sampled scenarios and evaluated in the optimization. The estimated objective value is called sample-average approximation (SAA) of the actual expected value. In this paper, Monte Carlo sampling and Latin hypercube sampling techniques are implemented. Confidence intervals of upper and lower bound are discussed. The method is implemented to an actual 12-area power system for generation expansion planning and transmission line expansion planning.

102 citations

Journal ArticleDOI
TL;DR: This work presents some chance constrained programming models for disassembly cost from the perspective of stochastic planning, and two hybrid intelligent algorithms are proposed to solve the proposed models.
Abstract: Disassembly planning is aimed to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. However, the actual disassembly process of products can experience great uncertainty due to a variety of unpredictable factors. To deal with such uncertainty, this work presents some chance constrained programming models for disassembly cost from the perspective of stochastic planning. Moreover, two hybrid intelligent algorithms, namely, one integrating stochastic simulation and neural network (NN), and another integrating stochastic simulation, genetic algorithm (GA) and neural network (NN), are proposed to solve the proposed models, respectively. Some numerical examples are given to illustrate the proposed models and the effectiveness of proposed algorithms.

102 citations

Journal ArticleDOI
TL;DR: In this article, an integrated operational model for electricity and natural gas systems under uncertain power supply by applying two-stage stochastic programming is proposed to optimize day-ahead and real-time dispatch of both energy systems and aims at minimizing the total expected cost.

102 citations

Journal ArticleDOI
Raghu Pasupathy1
TL;DR: This paper presents an overview of the conditions that guarantee the correct convergence of RA's iterates, and characterize a class of error-tolerance and sample-size sequences that are superior to others in a certain precisely defined sense.
Abstract: The stochastic root-finding problem is that of finding a zero of a vector-valued function known only through a stochastic simulation. The simulation-optimization problem is that of locating a real-valued function's minimum, again with only a stochastic simulation that generates function estimates. Retrospective approximation (RA) is a sample-path technique for solving such problems, where the solution to the underlying problem is approached via solutions to a sequence of approximate deterministic problems, each of which is generated using a specified sample size, and solved to a specified error tolerance. Our primary focus, in this paper, is providing guidance on choosing the sequence of sample sizes and error tolerances in RA algorithms. We first present an overview of the conditions that guarantee the correct convergence of RA's iterates. Then we characterize a class of error-tolerance and sample-size sequences that are superior to others in a certain precisely defined sense. We also identify and recommend members of this class and provide a numerical example illustrating the key results.

102 citations

Book
01 Jan 1968
TL;DR: In this paper, a comprehensive treatment of stochastic systems is presented, beginning with the foundations of probability and ending with optimal control, which leads to the solution of optimal control problems resulting in controllers with significant practical application.
Abstract: A comprehensive treatment of stochastic systems beginning with the foundations of probability and ending with stochastic optimal control. The book divides into three interrelated topics. First, the concepts of probability theory, random variables and stochastic processes are presented, which leads easily to expectation, conditional expectation, and discrete time estimation and the Kalman filter. With this background, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems resulting in controllers with significant practical application. This book will be valuable to first year graduate students studying systems and control, as well as professionals in this field.

102 citations


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