The aim of this research is to construct a three-dimensional maritime transport model to transport nonhomogeneous goods (k) and different transport modes (v) from their sources (i) to their destinations (j), while limiting the optimum quantities v ijk x to be transported at the lowest possible cost v ijk c and time v ijk t using the heuristic algorithm, Transport problems have been widely studied in computer science and process research and are one of the main problems of transport problems that are usually used to reduce the cost or times of transport of goods with a number of sources and a number of destinations and by means of transport to meet the conditions of supply and demand. Transport models are a key tool in logistics and supply chain management to reduce costs, times or improve services, In this study Three algorithms were proposed to solve the transport matrix (Range(R), Arithmetic Mean(AM), Cost Slop(CO)), and this algorithm must meet the requirements of problem restrictions and goals to reach good solutions, and may sometimes be the optimal solutions so we will adopt any solutions that are the best and optimal through our findings in the application of heuristic algorithms and based on the final results can be based on the heuristic method., The research concluded that the best reasoning method is the (arithmetic mean(AM)) because it gave the best results in reducing the total (cost and time) before and after the optimization method (MODI), It also gave the cost inclination method less total costs and time higher than the method of arithmetic mean After conducting the optimization method(MODI)
In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.
A genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure.
Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil wa
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This work addressed the assignment problem (AP) based on fuzzy costs, where the objective, in this study, is to minimize the cost. A triangular, or trapezoidal, fuzzy numbers were assigned for each fuzzy cost. In addition, the assignment models were applied on linguistic variables which were initially converted to quantitative fuzzy data by using the Yager’sorankingi method. The paper results have showed that the quantitative date have a considerable effect when considered in fuzzy-mathematic models.
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This research aims to learn about public relations programs and their role to enhance the morale of the State land Transport Company employees.The researcher relied on the survey method and use a questionnaire and scale tools to collect information from workers in the Department of Relations and Media and employees in all departments.
The research reached several conclusions, including:
1- Public relations seek to increase workers’ confidence in senior management and motivate them to improve their production, as well as their relentless endeavor to bring workers closer by following multiple and varied forms of communication with them.
2- The results of the study showed that there was a negative i
Circular thin walled structures have wide range of applications. This type of structure is generally exposed to different types of loads, but one of the most important types is a buckling. In this work, the phenomena of buckling was studied by using finite element analysis. The circular thin walled structure in this study is constructed from; cylindrical thin shell strengthen by longitudinal stringers, subjected to pure bending in one plane. In addition, Taguchi method was used to identify the optimum combination set of parameters for enhancement of the critical buckling load value, as well as to investigate the most effective parameter. The parameters that have been analyzed were; cylinder shell thickness, shape of stiffeners section an
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Segmented regression consists of several sections separated by different points of membership, showing the heterogeneity arising from the process of separating the segments within the research sample. This research is concerned with estimating the location of the change point between segments and estimating model parameters, and proposing a robust estimation method and compare it with some other methods that used in the segmented regression. One of the traditional methods (Muggeo method) has been used to find the maximum likelihood estimator in an iterative approach for the model and the change point as well. Moreover, a robust estimation method (IRW method) has used which depends on the use of the robust M-estimator technique in
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In this paper, we made comparison among different parametric ,nonparametric and semiparametric estimators for partial linear regression model users parametric represented by ols and nonparametric methods represented by cubic smoothing spline estimator and Nadaraya-Watson estimator, we study three nonparametric regression models and samples sizes n=40,60,100,variances used σ2=0.5,1,1.5 the results for the first model show that N.W estimator for partial linear regression model(PLM) is the best followed the cubic smoothing spline estimator for (PLM),and the results of the second and the third model show that the best estimator is C.S.S.followed by N.W estimator for (PLM) ,the
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Bootstrap is one of an important re-sampling technique which has given the attention of researches recently. The presence of outliers in the original data set may cause serious problem to the classical bootstrap when the percentage of outliers are higher than the original one. Many methods are proposed to overcome this problem such Dynamic Robust Bootstrap for LTS (DRBLTS) and Weighted Bootstrap with Probability (WBP). This paper try to show the accuracy of parameters estimation by comparison the results of both methods. The bias , MSE and RMSE are considered. The criterion of the accuracy is based on the RMSE value since the method that provide us RMSE value smaller than other is con
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