Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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TL;DR: The proposed SMPCL approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.
Abstract: This paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.
375 citations
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TL;DR: In this paper, the authors developed an asset/liability management model using multistage stochastic programming, which determines an optimal investment strategy that incorporates a multi-period approach and enables the decision makers to define risks in tangible operational terms.
Abstract: Frank Russell Company and The Yasuda Fire and Marine Insurance Co., Ltd., developed an asset/liability management model using multistage stochastic programming. It determines an optimal investment strategy that incorporates a multiperiod approach and enables the decision makers to define risks in tangible operational terms. It also handles the complex regulations imposed by Japanese insurance laws and practices. The most important goal is to produce a high-income return to pay annual interest on savings-type insurance policies without sacrificing the goal of maximizing the long-term wealth of the firm. During the first two years of use, fiscal 1991 and 1992, the investment strategy devised by the model yielded extra income of 42 basis points (¥8.7 billion or US$79 million).
371 citations
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01 Jan 2005TL;DR: It is argued that two-stage (linear) stochastic programming problems with recourse can be solved with a reasonable accuracy by using Monte Carlo sampling techniques, while multistage Stochastic programs, in general, are intractable.
Abstract: The main focus of this paper is in a discussion of complexity of stochastic programming problems. We argue that two-stage (linear) stochastic programming problems with recourse can be solved with a reasonable accuracy by using Monte Carlo sampling techniques, while multistage stochastic programs, in general, are intractable. We also discuss complexity of chance constrained problems and multistage stochastic programs with linear decision rules.
371 citations
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01 Jan 2011
TL;DR: In this paper, robust linear optimization problems with uncertainty regions defined by o-divergences (for example, chi-squared, Hellinger, Kullback-Leibler) are studied.
Abstract: Samenvatting In this paper we focus on robust linear optimization problems with uncertainty regions defined by o-divergences (for example, chi-squared, Hellinger, Kullback-Leibler). We show how uncertainty regions based on o-divergences arise in a natural way as confidence sets if the uncertain parameters contain elements of a probability vector. Such problems frequently occur in, for example, optimization problems in inventory control or finance that involve terms containing moments of random variables, expected utility, etc. We show that the robust counterpart of a linear optimization problem with o-divergence uncertainty is tractable for most of the choices of o typically considered in the literature. We extend the results to problems that are nonlinear in the optimization variables. Several applications, including an asset pricing example and a numerical multi-item newsvendor example, illustrate the relevance of the proposed approach.
369 citations
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TL;DR: A stochastic version of the interdictor's problem: Minimize the expected maximum flow through the network when interdiction successes are binary random variables is formulated and solved.
Abstract: Using limited assets, an interdictor attempts to destroy parts of a capacitated network through which an adversary will subsequently maximize flow. We formulate and solve a stochastic version of the interdictor's problem: Minimize the expected maximum flow through the network when interdiction successes are binary random variables. Extensions are made to handle uncertain arc capacities and other realistic variations. These two-stage stochastic integer programs have applications to interdicting illegal drugs and to reducing the effectiveness of a military force moving materiel, troops, information, etc., through a network in wartime. Two equivalent model formulations allow Jensen's inequality to be used to compute both lower and upper bounds on the objective, and these bounds are improved within a sequential approximation algorithm. Successful computational results are reported on networks with over 100 nodes, 80 interdictable arcs, and 180 total arcs.
367 citations