A simulation model and its application for planning swine facilities are presented. Swine production is becoming more and more specialized, hence the sizing of a farm producing piglets is the main strategic decision c...
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ISBN:
(纸本)0780395190
A simulation model and its application for planning swine facilities are presented. Swine production is becoming more and more specialized, hence the sizing of a farm producing piglets is the main strategic decision concerning farmers who invest in sow production, since a farm comprises a big range of facilities with many possible sizes. The classical approach is deterministic, including sometimes some security margins without considering variations in future sow performance or in the management policy. The stochastic model presented here has revealed practical differences with respect to deterministic approaches. As result, simulation is useful to determine accurately the capacity, improve farm design, prevent practical problems and fit housing cost. Furthermore, the implementation in Extend allows potential users to perform efficiently different kinds of analyses.
In this paper we introduce a novel notion of bisimulation to properly capture the behavior of stochastic systems with general distributions. The key idea consists in the identification of different sequences of random...
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ISBN:
(纸本)3540291075
In this paper we introduce a novel notion of bisimulation to properly capture the behavior of stochastic systems with general distributions. The key idea consists in the identification of different sequences of random variables if the additions of the random variables of each sequence are identically distributed. That is, we will not only identify sequences of internal actions with one of them (as it is usually done in weak bisimulations) but we will also reduce (in some conditions) sequences of stochastic transitions to only one transition. Therefore, we will identify processes that are considered non-equivalent in previous notions of bisimulation for this kind of languages.
Modern electricity portfolio and risk management models represent multistage stochastic programs. The input of such programs consists in a finite set of scenarios having the form of a scenario tree. They model the pro...
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ISBN:
(纸本)9781424418749
Modern electricity portfolio and risk management models represent multistage stochastic programs. The input of such programs consists in a finite set of scenarios having the form of a scenario tree. They model the probabilistic information on random data (electrical load, stream flows to hydro units, market prices of fuel and electricity). Since the corresponding deterministic equivalents of multistage stochastic programs are mostly large scale, one has to find significant tree-structured scenarios. Our approach to generate multivariate scenario trees is based on recursive deletion and bundling of scenarios out of some given (possibly large) scenario set originating from historical or simulated data. The procedure makes use of certain Monge-Kantorovich transportation distances for multivariate probability distributions. We report on computational results for generating load-inflow scenario trees based on realistic data of EDF Electricité de France.
In response to consumer demands, the range of products offered by manufacturersis becoming increasingly complex. One of the ways that companies are dealingwith this challenge is to offer products that can be assembled...
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In response to consumer demands, the range of products offered by manufacturersis becoming increasingly complex. One of the ways that companies are dealingwith this challenge is to offer products that can be assembled from acollection of independent components. One of the central difficulties facing themakers of such `configured-to-order' products is the following: The precisequantity and collection of resources required to satisfy an order is not knownuntil a customer has actually placed an order. The supply chain tools currentlyavailable to practitioners are not prepared to deal with such uncertainty andexisting academic models do not adequately address the problem in a practicalmanner. This thesis responds to this challenge by addressing a series ofoptimization problems and framing them within the context of a popular businessprocess called sales and operations *** this context, three related optimization models are formulated and solvedusing stochastic programming techniques. These models are referred to as theexplosion, implosion and component rationing problems, respectively. Theexplosion and implosion problems both seek to maximize profit, but where theexplosion model determines component requirements given product demand, theimplosion model determines the appropriate product sales targets given certainrestrictions on component supply. The component rationing problem seeks tomaximize revenue for a given component supply and product demand byappropriately setting the component threshold levels that will reserve certaincomponents for certain product orders. In all three problems, uncertainty ispresent because the quantity of components required to satisfy an order isunknown at the time that the decisions are *** studies performed using problem sets derived from data provided byIBM show that, with respect to the explosion and implosion problems, there issignificant benefit to accounting for the uncertainty associated with howproducts a
We propose a new method for certain multistage stochastic programs with linear or nonlinear objective function, combining a primal interior point approach with a linear-quadratic control problem over the scenario tree...
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We propose a new method for certain multistage stochastic programs with linear or nonlinear objective function, combining a primal interior point approach with a linear-quadratic control problem over the scenario tree. The latter problem, which is the direction finding problem for the barrier subproblem is solved through dynamic programming using Riccati equations. In this way we combine the low iteration count of interior point methods with an efficient solver for the subproblems. The computational results are promising, We have solved a financial problem with 1,000,000 scenarios, 15,777,740 variables and 16,888,850 constraints in 20 hours on a moderate computer. (C) 2002 Elsevier Science B.V. All rights reserved.
When solving a decision problem under uncertainty via stochastic programming it is essential to choose or to build a suitable stochastic programming model taking into account the nature of the real-life problem, chara...
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When solving a decision problem under uncertainty via stochastic programming it is essential to choose or to build a suitable stochastic programming model taking into account the nature of the real-life problem, character of input data, availability of software and computer technology. Besides a brief review of history and achievements of stochastic programming, selected modeling issues concerning applications of multistage stochastic programs with recourse (the choice of the horizon, stages, methods for generating scenario trees, etc.) will be discussed. (C) 2002 Published by Elsevier Science B.V.
in this paper, a robust scheduling method is suggested in the optimization of batch plant with uncertainties considering not only expected value but also variance. Many papers treating scenario-based stochastic progra...
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We consider an added level of adaptation to classical weighted least-squares (WLS) by adapting the forgetting factor a using a stochastic approximation (SA) algorithm, thus obtaining the modified WLS algorithm. The SA...
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This paper illustrates an application of Monte Carlo Optimization to derive monthly reservoir operating rules. The procedure generates synthetic inflow scenarios which are used by a deterministic optimization model. T...
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This paper is concerned with behavior analyses of the stochastic mean curvature flows of some phase boundaries. The phase boundary is defined as a curve or a surface separating different physical states such as a wate...
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