Matrices are widely used in transportation planning to represent the distribution of characteristics or as origin-destination matrices. Developing such matrices by means of surveys is expensive and time consuming, and...
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Matrices are widely used in transportation planning to represent the distribution of characteristics or as origin-destination matrices. Developing such matrices by means of surveys is expensive and time consuming, and once the survey data are collected and compiled the matrices are rapidly outdated. Other methods which are commonly used are unable to include all available data or to provide a measure of the uncertainty of the estimates. This paper formulates a quadratic programming method to estimate matrix entry estimates as an equivalent constrained generalized least squares estimation problem. As well as being able to include any available information in the form of constraints, the variance-covariance matrix of the entry estimates may be found and confidence intervals calculated for matrix entry estimates with some added distributional assumptions. The problem of updating the proportions of nationwide automobile trips by purpose and trip length from 1970 to 1977 is included as a simple example to illustrate the method.
A set of n customers is given. Each customer has a desired point of pickup, a desired point of delivery and a desired time of delivery. The problem is to determine the order of pickup and delivery and the times of pic...
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A set of n customers is given. Each customer has a desired point of pickup, a desired point of delivery and a desired time of delivery. The problem is to determine the order of pickup and delivery and the times of pickup and delivery of these n customers by a single vehicle in order to minimize total customer inconvenience. Here, a mathematical programming formulating of this problem is subjected to Benders' decomposition procedure. The result is a heuristic routing and scheduling algorithm which is shown to produce high quality solutions in reasonable computation time by testing on moderately sized real data bases from both Gaithers-burg, Maryland, and Baltimore, Maryland. This study is divided into two parts, the first detailing the scheduling analysis and the second focusing on the routing component.
A basic hypothesis is proposed: given that wavelet-based analysis has been used to interpret runoff time-series, it may be extended to evaluation of rainfall-runoff model results. Conventional objective functions make...
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A basic hypothesis is proposed: given that wavelet-based analysis has been used to interpret runoff time-series, it may be extended to evaluation of rainfall-runoff model results. Conventional objective functions make certain assumptions about the data series to which they are applied (e.g. uncorrelated error, homoscedasticity). The difficulty that objective functions have in distinguishing between different realizations of the same model, or different models of the same system, is that they may have contributed in part to the occurrence of model equifinality. Of particular concern is the fact that the error present in a rainfall-runoff model may be time dependent, requiring some form of time localization in both identification of error and derivation of global objective functions. We explore the use of a complex Gaussian (order 2) wavelet to describe: (1) a measured hydrograph;(2) the same hydrograph with different simulated errors introduced;and (3) model predictions of the same hydrograph based upon a modified form of TOPMODEL. The analysis of results was based upon: (a) differences in wavelet power (the wavelet power error) between the measured hydrograph and both the simulated error and modelled hydrographs;and (b) the wavelet phase. Power difference and wavelet phase were used to develop two objective functions, RMSE(power) and RMS(phase), which were shown to distinguish between simulated errors and model predictions with similar values of the commonly adopted Nash-Sutcliffe efficiency index. These objective functions suffer because they do not retain time, frequency or time-frequency localization. Consideration of wavelet power spectra and time- and frequency-integrated power spectra shows that the impacts of different types of simulated error can be seen through retention of some localization, especially in relation to when and the scale over which error was manifest. Theoretical objections to the use of wavelet analysis for this type of application are note
Necessary and sufficient optimality conditions, requiring no constraint qualifications, are given in terms of associated linear programs which also serve in the characterization of, and the reduction to, canonical pro...
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Necessary and sufficient optimality conditions, requiring no constraint qualifications, are given in terms of associated linear programs which also serve in the characterization of, and the reduction to, canonical programs. An iterative method for solving superconsistent geometric programs, based on the above conditions, is described.
This paper describes some experiments with two alternative formulations of a nonlinear programming model. One apparently suitable formulation proves to be numerically disastrous for several algorithms. The paper discu...
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This paper describes some experiments with two alternative formulations of a nonlinear programming model. One apparently suitable formulation proves to be numerically disastrous for several algorithms. The paper discusses the reasons for this behavior and argues that solution systems need to be strengthened so that they can cope with alternative model formulations. As argued in the paper, it would be desirable to improve the numerical algorithms themselves, or to develop computerized modeling systems that can reformulate models into the form most suitable for a particular existing algorithm. [ABSTRACT FROM AUTHOR]
A method of scheduling new product evaluation steps is proposed. The method presented offers promise in reducing the costs of this evaluation. The value of each evaluation is based on Bayesian methods.
A method of scheduling new product evaluation steps is proposed. The method presented offers promise in reducing the costs of this evaluation. The value of each evaluation is based on Bayesian methods.
Practical procedures for statistical inference about the parameters of empirical models are presented. These avoid the necessity of formulating an explicit statistical model and can allow for general statistical depen...
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Practical procedures for statistical inference about the parameters of empirical models are presented. These avoid the necessity of formulating an explicit statistical model and can allow for general statistical dependency within the data used for model fitting. The theory is an asymptotic one, and one which allows the construction of significance tests and confidence regions for subsets of the model parameters. Other results concern the comparison of structurally different models, and of different objective functions for parameter fitting.
A novel approach to mathematical modeling of chromatographic responses based on interpolation polynomials with divided differences and with finite differences is discussed. These interpolational techniques as well as ...
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A novel approach to mathematical modeling of chromatographic responses based on interpolation polynomials with divided differences and with finite differences is discussed. These interpolational techniques as well as traditionally applied second-order polynomial models obtained by least squares are compared. Interpolation techniques can be useful in situations where commonly used linear or quadratic models are not applicable: when the nature of dependence is complex or the investigated factor intervals are broad. The three analyzed modeling techniques are incorporated in a design of experiments methodology for systematic development and optimization of liquid chromatographic methods. The direct modeling of retention factors is carried out first, while the objective function for final quality measurement is calculated last. An interpolation polynomial with divided differences resulted in a high quality fit compared with the results obtained by the other two modeling approaches and succeeded in locating the desired optimum. It is shown that this modeling technique can be a useful alternative for modeling of chromatographic responses. Copyright (c) 2013 John Wiley & Sons, Ltd.
This paper describes the application of a complete MBPC solution for existing HVAC systems, with a focus on the implementation of the objective function employed. Real-time results obtained with this solution, in term...
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This paper presents a unified development of a surrogate duality theory that is applicable to problems in which Lagrangean duality gaps limit the usefulness of standard duality approaches. A surrogate dual is created ...
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This paper presents a unified development of a surrogate duality theory that is applicable to problems in which Lagrangean duality gaps limit the usefulness of standard duality approaches. A surrogate dual is created by generating a single constraint to replace the original problem constraints, rather than by absorbing these constraints into the objective function as in the Lagrangean. We give necessary and sufficient conditions for optimality both with and without the imposition of complementary slackness, and also consider a related “overestimating” surrogate that may be used in a strategy to bracket the optimal value of the primal. The optimality conditions invite direct comparison with those for Lagrangean duality, demonstrating not only that the surrogate approach yields smaller duality gaps than the Lagrangean (as first observed by Greenberg and Pierskalla), but also giving a precise characterization of the manner and extent to which this occurs. Concepts of parametric and relative subgradients, paralleling (and generalizing) the concept of the subgradient of ordinary duality theory, lead to easily stated results that encompass both surrogate and Lagrangean duality, as well as their composite, in a single framework.
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