Contribution margin model applied to the marketing planning of a container liner carrier

The implementation of confidential contracts between a container liner carrier and its customers, because of the Ocean Shipping Reform Act (OSRA) 1998, demands a revision in the methodology applied in the carrier's planning of marketing and sales. The marketing and sales planning process should be more scientific and with a better use of operational research tools considering the selection of the customers under contracts, the duration of the contracts, the freight, and the container imbalances of these contracts are basic factors for the carrier's yield. This work aims to develop a decision support system based on a linear programming model to generate the business plan for a container liner carrier, maximizing the contribution margin of its freight.

Real time optimization (RTO) with model predictive control (MPC)

This paper studies a simplified methodology to integrate the real time optimization (RTO) of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. Optimal conditions of the process at steady state are searched through the use of a rigorous non-linear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model, obtained through a plant step test. The main advantage of the proposed strategy is that the resulting control/optimization problem can still be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed may be comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a non-linear programming problem with a much higher computer load. (C) 2010 Elsevier Ltd. All rights reserved.

Time stability of soil water storage measured by neutron probe and the effects of calibration procedures in a small watershed

The knowledge of soil water storage (SWS) of soil profiles is crucial for the adoption of vegetation restoration practices. With the aim of identifying representative sites to obtain the mean SWS of a watershed, a time stability analysis of neutron probe evaluations of SWS was performed by the means of relative differences and Spearman rank correlation coefficients. At the same time, the effects of different neutron probe calibration procedures were explored on time stability analysis. mean SWS estimation. and preservation of the spatial variability of SWS. The selected watershed, with deep gullies and undulating slopes which cover an area of 20 ha, is characterized by an Ust-Sandiic Entisol and an Aeolian sandy soil. The dominant vegetation species are bunge needlegrass (Stipa bungeana Trim) and korshinsk peashrub (Carugano Korshinskii kom.). From June 11, 2007 to July 23,2008, SWS of the top1 m soil layer was evaluated for 20 dates, based on neutron probe data of 12 sampling sites. Three calibration procedures were employed: type 1, most complete, with each site having its own linear calibration equation (TrE); type II. with TrE equations extended over the whole field: and type III, with one single linear calibration curve for the whole field (UnE) and also correcting its intercept based on site specific relative difference analysis (RdE) and on linear fitting of data (RcE), both maintaining the same slope. A strong time stability of SWS estimated by TrE equations was identified. Soil particle size and soil organic matter content were recognized as the influencing factors for spatial variability of SWS. Land use influenced neither the spatial variability nor the time stability of SWS. Time stability analysis identified one site to represent the mean SWS of the whole watershed with mean absolute percentage errors of less than 10%, therefore. this site can be used as a predictor for the mean SWS of the watershed. Some equations of type II were found to be unsatisfactory to yield reliable mean SWS values or in preserving the associated soil spatial variability. Hence, it is recommended to be cautious in extending calibration equations to other sites since they might not consider the field variability. For the equations with corrected intercept (type III), which consider the spatial variability of calibration in a different way in relation to TrE, it was found that they can yield satisfactory means and standard deviation of SWS, except for the RdE equations, which largely leveled off the SWS values in the watershed. Correlation analysis showed that the neutron probe calibration was linked to soil bulk density and to organic matter content. Therefore, spatial variability of soil properties should be taken into account during the process of neutron probe calibration. This study provides useful information on the mean SWS observation with a time stable site and on distinct neutron probe calibration procedures, and it should be extended to soil water management studies with neutron probes, e.g., the process of vegetation restoration in wider area and soil types of the Loess Plateau in China. (C) 2009 Elsevier B.V. All rights reserved.

Likelihood-based estimation of the regression model with scrambled responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator outperforms the classical estimators in almost all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Interlaboratory validation of an environmental monitoring method for trace analysis of endocrine disrupting compounds

Environmental pollution continues to be an emerging study field, as there are thousands of anthropogenic compounds mixed in the environment whose possible mechanisms of toxicity and physiological outcomes are of great concern. Developing methods to access and prioritize the screening of these compounds at trace levels in order to support regulatory efforts is, therefore, very important. A methodology based on solid phase extraction followed by derivatization and gas chromatography-mass spectrometry analysis was developed for the assessment of four endocrine disrupting compounds (EDCs) in water matrices: bisphenol A, estrone, 17b-estradiol and 17a-ethinylestradiol. The study was performed, simultaneously, by two different laboratories in order to evaluate the robustness of the method and to increase the quality control over its application in routine analysis. Validation was done according to the International Conference on Harmonisation recommendations and other international guidelines with specifications for the GC-MS methodology. Matrix-induced chromatographic response enhancement was avoided by using matrix-standard calibration solutions and heteroscedasticity has been overtaken by a weighted least squares linear regression model application. Consistent evaluation of key analytical parameters such as extraction efficiency, sensitivity, specificity, linearity, limits of detection and quantification, precision, accuracy and robustness was done in accordance with standards established for acceptance. Finally, the application of the optimized method in the assessment of the selected analytes in environmental samples suggested that it is an expedite methodology for routine analysis of EDC residues in water matrices.

Nonlinear mixture model for hyperspectral unmixing

Proceedings of International Conference - SPIE 7477, Image and Signal Processing for Remote Sensing XV - 28 September 2009

Retroanálise de parâmetros geomecânicos baseada em algoritmos de otimização

Dissertação de mestrado integrado em Engenharia Civil

Encapsulation and controlled release of bioactive compounds in lactoferrin-glycomacropeptide nanohydrogels : curcumin and caffeine as model compounds

Curcumin and caffeine (used as lipophilic and hydrophilic model compounds, respectively) were successfully encapsulated in lactoferrin-glycomacropeptide (Lf-GMP) nanohydrogels by thermal gelation showing high encapsulation efficiencies (>90 %). FTIR spectroscopy confirmed the encapsulation of bioactive compounds in Lf-GMP nanohydrogels and revealed that according to the encapsulated compound different interactions occur with the nanohydrogel matrix. The successful encapsulation of bioactive compounds in Lf-GMP nanohydrogels was also confirmed by fluorescence measurements and confocal laser scanning microscopy. TEM images showed that loaded nanohydrogels maintain their spherical shape with sizes of 112 and 126 nm for curcumin and caffeine encapsulated in Lf-GMP nanohydrogels, respectively; in both cases a polydispersity of 0.2 was obtained. The release mechanisms of bioactive compounds through Lf-GMP nanohydrogels were evaluated at pH 2 and pH 7, by fitting the Linear Superimposition Model to the experimental data. The bioactive compounds release was found to be pH-dependent: at pH 2, relaxation is the governing phenomenon for curcumin and caffeine compounds and at pH 7 Ficks diffusion is the main mechanism of caffeine release while curcumin was not released through Lf-GMP nanohydrogels.

Determination of displacement, stress- and strain-distribution in the human heart: a FE-model on the basis of MR imaging.

The determination of characteristic cardiac parameters, such as displacement, stress and strain distribution are essential for an understanding of the mechanics of the heart. The calculation of these parameters has been limited until recently by the use of idealised mathematical representations of biventricular geometries and by applying simple material laws. On the basis of 20 short axis heart slices and in consideration of linear and nonlinear material behaviour we have developed a FE model with about 100,000 degrees of freedom. Marching Cubes and Phong's incremental shading technique were used to visualise the three dimensional geometry. In a quasistatic FE analysis continuous distribution of regional stress and strain corresponding to the endsystolic state were calculated. Substantial regional variation of the Von Mises stress and the total strain energy were observed at all levels of the heart model. The results of both the linear elastic model and the model with a nonlinear material description (Mooney-Rivlin) were compared. While the stress distribution and peak stress values were found to be comparable, the displacement vectors obtained with the nonlinear model were generally higher in comparison with the linear elastic case indicating the need to include nonlinear effects.

Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty

This paper extends the Nelson-Siegel linear factor model by developing a flexible macro-finance framework for modeling and forecasting the term structure of US interest rates. Our approach is robust to parameter uncertainty and structural change, as we consider instabilities in parameters and volatilities, and our model averaging method allows for investors' model uncertainty over time. Our time-varying parameter Nelson-Siegel Dynamic Model Averaging (NS-DMA) predicts yields better than standard benchmarks and successfully captures plausible time-varying term premia in real time. The proposed model has significant in-sample and out-of-sample predictability for excess bond returns, and the predictability is of economic value.

A New Model for Designing a Product Line Using TURF Analysis

This paper introduces the approach of using TURF analysis to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

Importância da Programação Linear na Determinação de Informações de Gestão - Caso Moave, Sa

A Investigação Operacional vem demonstrando ser uma valiosa ferramenta de gestão nos dias de hoje em que se vive num mercado cada vez mais competitivo. Através da Programação Linear pode-se reproduzir matematicamente um problema de maximização dos resultados ou minimização dos custos de produção com o propósito de auxiliar os gestores na tomada de decisão. A Programação Linear é um método matemático em que a função objectivo e as restrições assumem características lineares, com diversas aplicações no controlo de gestão, envolvendo normalmente problemas de utilização dos recursos disponíveis sujeitos a limitações impostas pelo processo produtivo ou pelo mercado. O objectivo geral deste trabalho é o de propor um modelo de Programação Linear para a programação ou produção e alocação de recursos necessários. Optimizar uma quantidade física designada função objectivo, tendo em conta um conjunto de condicionalismos endógenas às actividades em gestão. O objectivo crucial é dispor um modelo de apoio à gestão contribuindo assim para afectação eficiente de recursos escassos à disposição da unidade económica. Com o trabalho desenvolvido ficou patente a importância da abordagem quantitativa como recurso imprescindível de apoio ao processo de decisão. The operational research has proven to be a valuable management tool today we live in an increasingly competitive market. Through Linear Programming can be mathematically reproduce a problem of maximizing performance or minimizing production costs in order to assist managers in decision making. The Linear Programming is a mathematical method in which the objective function and constraints are linear features, with several applications in the control of management, usually involving problems of resource use are available subject to limitations imposed by the production process or the market. The overall objective of this work is to propose a Linear Programming model for scheduling or production and allocation of necessary resources. Optimizing a physical quantity called the objective function, given a set of endogenous constraints on management thus contributing to efficient allocation of scarce resources available to the economic unit. With the work has demonstrated the importance of the quantitative approach as essential resource to support the decision process.