Recently, various types of neural network models have been used successfully to applications in pattern recognition, control, signal processing, and so on. However, the previous models are not suitable for hardware im...
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A bounding-based method is developed for estimating the expected operation cost of a multiarea electric power system in which transmission capacity limits interarea flows. Costs include the expense of power generation...
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A bounding-based method is developed for estimating the expected operation cost of a multiarea electric power system in which transmission capacity limits interarea flows. Costs include the expense of power generation and losses suffered by consumers because of supply shortfalls, averaged over random generator outage states and varying demand levels. The calculation of this expectation, termed the distribution problem, is a large-scale stochastic programming problem. Rather than solving this problem directly, lower and upper bounds to the expected cost are created using two more easily solved models. The lower bound is from a deterministic model based on the expected value of the uncertain inputs. The upper bound results from a linear program with recourse whose structure permits relatively quick solution by Benders decomposition. The Benders subproblems use probabilistic production costing, which can be viewed as a stochastic greedy algorithm, to consider random outages and demands. These bounds are iteratively tightened by partitioning realizations of the random variables into subsets based on the status of larger generators and a cluster analysis of demands. Computational examples are described and application issues addressed.
In this paper the maintenance scheduling problem is cast in a stochastic framework. Then, a stochastic programming model with recourse is developed for the problem. The model is a stochastic version of Robert and Escu...
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In this paper the maintenance scheduling problem is cast in a stochastic framework. Then, a stochastic programming model with recourse is developed for the problem. The model is a stochastic version of Robert and Escudero model for scheduling maintenance personnel. An illustrative example is given to demonstrate the utility of the model, and the Value of the stochastic solution is calculated and it showed about 10% improvement over the deterministic formulation of the maintenance scheduling problem. (C) 1999 Elsevier Science Inc. All rights reserved.
From the point of view of quality management, it is an important issue to develop some performance indexes for a flow network. This paper studies the performance indexes for a stochastic-flow network in which each arc...
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From the point of view of quality management, it is an important issue to develop some performance indexes for a flow network. This paper studies the performance indexes for a stochastic-flow network in which each arc has several possible capacities. For the case that multiple types of commodity are transmitted through the same stochastic-flow network, this paper defines the system capacity as a vector and then proposes a new performance index. An algorithm in terms of minimal cuts is proposed to evaluate such a performance index.
This paper presents modeling techniques for planning electrical generation capacity where demand is uncertain. We present traditional solutions to the capacity generation problem using a deterministic programming mode...
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We model the Danish market for mortgage backed securities with a two-factor interest rate model and use a stochastic programming approach to analyse how an individual home-owner should initially compose and subsequent...
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Solutions of portfolio optimization problems are often influenced by errors or misspecifications due to approximation, estimation and incomplete information. Selected methods for analysis of results obtained by solvin...
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Solutions of portfolio optimization problems are often influenced by errors or misspecifications due to approximation, estimation and incomplete information. Selected methods for analysis of results obtained by solving stochastic programs are presented and their scope illustrated on generic examples - the Markowitz model a multiperiod bond portfolio management problem and a general strategic investment problem. The approaches are based on asymptotic and robust statistics, on the moment problem and on results of parametric optimization.
Quantitative modelling of the asset-liability management (ALM) problem faced by banking institutions is reexamined in this paper. The model presented here joins stochastic programming with non-linear goal programming ...
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stochastic programming provides an effective framework for addressing decision problems under uncertainty in diverse fields. stochastic programs incorporate many possible contingencies so as to proactively account for...
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stochastic programming provides an effective framework for addressing decision problems under uncertainty in diverse fields. stochastic programs incorporate many possible contingencies so as to proactively account for randomness in their input data;thus, they inevitably lead to very large optimization programs. Consequently, efficient algorithms that can exploit the capabilities of advanced computing technologies - including multiprocessor computers - become imperative to solve large-scale stochastic programs. This paper surveys the state-of-the-art in parallel algorithms for stochastic programming. Algorithms are reviewed, classified and compared. Qualitative comparisons are based on the applicability, scope, ease of implementation, robustness and reliability of each algorithm, while quantitative comparisons are based on the computational performance of algorithmic implementations on multiprocessor systems. Emphasis is placed on the potential of parallel algorithms to solve large-scale stochastic programs.
A model of multistage stochastic programming over a scenario tree is developed, in which the evolution of information states, as represented by the nodes of a scenario tree, is supplemented by a dynamical system of st...
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A model of multistage stochastic programming over a scenario tree is developed, in which the evolution of information states, as represented by the nodes of a scenario tree, is supplemented by a dynamical system of state vectors controlled by recourse decisions. A dual problem is obtained in which multipliers associated with the primal dynamics are price vectors that are propagated backward in time through a dual dynamical system involving conditional expectation. A format of Fenchel duality is employed in order to have immediate specialization not only to linear programming but also to extended linear-quadratic programming. The resulting optimality conditions support schemes of decomposition in which a separate optimization problem is solved at each node of the scenario tree.
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