Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on a scenario model of future environment developments. A good approximation of t...
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ISBN:
(纸本)9780955301827
Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on a scenario model of future environment developments. A good approximation of the underlying stochastic process may involve a very large number of scenarios and their probabilities. We discuss the case when enough data paths can be generated, but due to solvability of stochastic program the scenario tree has to be constructed. The proposed strategy is to generate the multistage scenario tree from the set of individual scenarios by bundling scenarios based on cluster analysis. The K-means clustering approach is modified to capture the interstage dependencies in order to model the sequential decisions. The described scenario tree generation method is implemented on sampled data of nominal interest rate.
A two-echelon half-closed-loop logistics network system is founded for the issue of manufacturing logistics network design, including supply and return channels. Considering the integration of the uncertainty and cycl...
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ISBN:
(纸本)9781424413119
A two-echelon half-closed-loop logistics network system is founded for the issue of manufacturing logistics network design, including supply and return channels. Considering the integration of the uncertainty and cycle time of the demand quantities of products an the return rates of used products, sites selection of potential logistics centers, plants and recycling plants, and transshipment problem, a stochastic, multi-time-step, capacitated mixed integer linear programming model is presented, the objective function makes total costs including fixed costs, transportation costs and storage costs in logistics centers minimize. Since such network design problems belong to a class of NP hard problems, a genetic algorithm approach-based heuristic to this model is presented Finally a numerical example is used to prove the model and genetic algorithm validity.
In this paper, we present a novel fair queue management algorithm called stochastic RED (StoRED), inspired by the well known stochastic fair queuing and based on the random early detection (RED) scheme. By extensive s...
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ISBN:
(纸本)9781424403523
In this paper, we present a novel fair queue management algorithm called stochastic RED (StoRED), inspired by the well known stochastic fair queuing and based on the random early detection (RED) scheme. By extensive simulations, we show the versatility of StoRED in disciplining misbehaving flows and achieving adjustable fairness in a variety of applications where there is a need to prevent unresponsive flows from overwhelming others. In another application, StoRED can also be invoked to improve the performance of Web traffic by reducing the probability of experiencing packet losses for such traffic. StoRED turns out to be an effective and practical algorithm that is ready for deployment.
An analysis of convex stochastic programs is provided when the underlying probability distribution is subjected to (small) perturbations. It is shown, in particular, that epsilon-approximate solution sets of convex st...
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An analysis of convex stochastic programs is provided when the underlying probability distribution is subjected to (small) perturbations. It is shown, in particular, that epsilon-approximate solution sets of convex stochastic programs behave Lipschitz continuously with respect to certain distances of probability distributions that are generated by the relevant integrands. It is shown that these results apply to linear two-stage stochastic programs with random recourse. We discuss the consequences on associating Fortet-Mourier metrics to two-stage models and on the asymptotic behavior of empirical estimates of such models, respectively.
In this paper, we propose an extended local search framework to solve combinatorial optimization problems with data uncertainty. Our approach represents a major departure from scenario-based or stochastic programming ...
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ISBN:
(纸本)9781577353447
In this paper, we propose an extended local search framework to solve combinatorial optimization problems with data uncertainty. Our approach represents a major departure from scenario-based or stochastic programming approaches often used to tackle uncertainty. Given a value 0
This paper presents a new derivation of Akcasu's Modified Levermore-Pomraning (MLP) model for estimating the ensemble-averaged angular flux for particle transport problems in 1-D geometrically random media. The si...
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ISBN:
(纸本)0894480596
This paper presents a new derivation of Akcasu's Modified Levermore-Pomraning (MLP) model for estimating the ensemble-averaged angular flux for particle transport problems in 1-D geometrically random media. The significant new feature of the MLP equations is that, unlike the earlier Levermore-Pomraning (LP) model, the MLP equations are exact for certain classes of problems with scattering.
The aim of this paper is to introduce and investigate a new type of probabilistic optimal topology design method where the elements of the loading are given randomly and they can have linear relationship. In the propo...
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In this paper, we propose a new way to model the discrete customer choice behavior through constructing a few preference orders representing the gross customer choice behavior. This approach turns the customer choices...
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In this paper, we propose a new way to model the discrete customer choice behavior through constructing a few preference orders representing the gross customer choice behavior. This approach turns the customer choices model into a stochastic programming problem. Our model descriptions show that modeling customer choices by preference orders would be more solvable in a large scale network. In addition, we show our heuristics, called backup, as the way to properly apply the seat allocation regarding highly volatile demands. Numerical results are presented to illustrate the effectiveness of the combination of preference orders and our backup heuristic.
The proceedings contain 530 papers. The topics discussed include: a class of facility location model and its application;dynamic stochastic programming for asset allocation problem;a multiplicative optimization model ...
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ISBN:
(纸本)1424415292
The proceedings contain 530 papers. The topics discussed include: a class of facility location model and its application;dynamic stochastic programming for asset allocation problem;a multiplicative optimization model for constructing composite indicators;modeling software integration scenarios for telecommunications operations software vendors;developing a systematic method for constructing the function platform of product family;prioritization of competitive priority in cleaner production implementation;new product portfolio selection using fuzzy logic;binomial distribution based approach to deriving time series weights;the analysis and the solving of local protectionism in passenger transportation between adjacent cities based on game theory;a model of manufacturing quality information supporting design;a study on a transportation market with empty equipment repositioning;and balance score card and social responsibility in public organizations.
stochastic integer programs (SIPs) represent a very difficult class of optimization problems arising from the presence of both uncertainty and discreteness in planning and decision problems. Although applications of S...
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stochastic integer programs (SIPs) represent a very difficult class of optimization problems arising from the presence of both uncertainty and discreteness in planning and decision problems. Although applications of SIPs are abundant, nothing is available by way of computational software. On the other hand, commercial software packages for solving deterministic integer programs have been around for quite a few years, and more recently, a package for solving stochastic linear programs has been released. In this paper, we describe how these software tools can be integrated and exploited for the effective solution of general-purpose SIPs. We demonstrate these ideas on four problem classes from the literature and show significant computational advantages.
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