Solving practical waste collection with time windows in an urban area

In logistics, several algorithms can be implemented in order to solve the problems of the vehicle routing with variants in order to find near-optimal solutions. Waste Collection can be considered as an essential logistic activity within any area or city. This kind of paper is aimed to implement Iterated greedy (IG) and Adaptive Large Neighborhood Search (ALNS) to solve waste collection vehicle routing problem with time windows on a real-case study. The idea is to generate an efficient way to collect waste problems in an area located in Riyadh, Saudi Arabia. Moreover, generating a route plays a significant role in terms of serving all customers’ demands who have own different time windows of receiving goods. Also, the performance of the proposed algorithms according to all instances is examined and minimizing the total costs and meeting all constraints that related to capacity, time windows, and others. To evaluate the execution of the presented algorithms, the computational results showed essential improvements, and also ALNS algorithm generates reasonable solutions in terms of total costs and a reasonable amount of time, when compared to other algorithms.

Using iterated greedy and randomized iterated greedy algorithms to solve urban area waste collection in Riyadh city

This paper addresses the real-life waste collection vehicle routing problem by applying Iterated Greedy (IG) and Randomized Iterated Greedy (RIG) in order to improve the processes. This kind of problem becomes more complex in developing countries in several aspects such as costs and fuel. Nowadays, the waste collection is considered as one of the interesting areas. There are three types of waste: commer-cial, residential and roll-on-roll-off. In this paper, we mainly consider the residential waste collection problem. The problem can be summa-rized as follows: a vehicle has to satisfy the demand at each customer location while satisfying the capacity of the vehicle for reducing the total cost. We report a case study that is related to waste collection in Riyadh, Kingdom of Saudi Arabia. To solve the case study problem, IG and RIG were employed. Experiments have been done on the case study data and show a better performance when compared IG algo-rithm results with RIG algorithm results.

Employing particle swarm optimization algorithm for shrinkage parameter estimation in generalized Liu estimator

It is well-known that in the presence of multicollinearity, the Liu estimator is an alternative to the ordinary least square (OLS) estimator and the ridge estimator. Generalized Liu estimator (GLE) is a generalization of the Liu estimator. However, the efficiency of GLE depends on appropriately choosing the shrinkage parameter matrix which is involved in the GLE. In this paper, a particle swarm optimization method, which is a metaheuristic continuous algorithm, is proposed to estimate the shrinkage parameter matrix. The simulation study and real application results show the superior performance of the proposed method in terms of prediction error.

Gene selection in Cox regression model based on a new adaptive penalized method

The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox proportional hazards regression model is the most popular model in regression analysis for censored survival data. In this paper, an adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazards regression model with the weighted least absolute shrinkage and selection operator (LASSO) method. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.

Network analysis of countries’ partnership in European sports programs: Erasmus+ sport

In the present work, data analysis of Erasmus+ Sport programs was performed using Network Theory. Funding amounts and partner coun-tries per program are the information of the target data. Developing a Python-based program, a network of countries' partnerships has been developed to examine whether specific countries cooperate more frequently, and which countries participate in more Erasmus+ Sport pro-grams. Thus, some basic indicators of centrality from network theory were calculated, which are presented together with their mathematical interpretation.It has also been studied whether the number of a country's participation in these programs is affected by its economic or social national characteristics. Specifically, GDP, happiness and education indexes are all examined if they affect a country's participation. Finally, given how the funding amount of a program is split between the partner countries, the total amount of funding received by each country for the period 2014-2018 was calculated.

Construction of twenty-six points specific optimum second order rotatable designs in three dimensions with a practical example

This quadratic response surface methodology focuses on finding the levels of some (coded) predictor variables x = (x1u, x2u, x3u)' that optimize the expected value of a response variable yu from natural levels. The experiment starts from some best guess or “control” combination of the predictor variables (usually coded to x = 0 for this case x1u=30, x2u=25 and x3u =40) and experiment is performed varying them in a region around this center point.We go further to construct a specific optimum second order rotatable design of three factors in twenty-six points. The achievement of this is done with estimation of the free parameters using calculus in an existing second order rotatable design of twenty-six points. Such a design permits a response surface to be fitted easily and provides spherical information contours besides the realizations of optimum combination of ingredients in Agriculture, horticulture and allied sciences which results in economic use of scarce resources in relevant production processes. The expected second order rotatable design model in three dimensions is available where the responses would then facilitate the estimation of the linear and quadratic coefficients. An example involving Phosphate (x1u), Nitrogen (x2u) and Potassium (x3u) is used to represent the three factors in the coded level and converted into natural levels.

Logarithmic Ratio and Product-type Estimators of Population Mean

Based on the natural logarithm of known population mean of an auxiliaryvariable, x, the study introduces logarithmic ratio and product-type estimatorsof the population mean of the study variable, y, in simple random samplingwithout replacement (SRSWOR) scheme. Part of the eciency conditions forthe proposed logarithmic estimators to be more ecient than the existing ex-ponential ratio and product-type estimators, as well as the customary ratio andproduct-type estimators, is that the natural logarithm of the known populationmean of the auxiliary variable, x, must be greater than 2. Generally, there is ahigh tendency for the proposed logarithmic estimators to be more ecient thanexisting customary and exponential ratio and product-type estimators whenthe natural logarithm of the auxiliary variable population mean is greater than2. The theoretical results are illustrated and conrmed using some numericaldatasets.

Fitting a Gamma Distribution using a Chi-squared Approach to the Heights of Students of Akwa Ibom State University, Nigeria

This paper fits a gamma probability model to the heights of Students of the Akwa Ibom State University. A sample of 998 Students was drawn from the Medical Centre of the Institution’s Main Campus, Ikot Akpaden, Akwa Ibom State. Some exploratory data analyses were carried out to observe the behavior of the data set graphically. A chi-square test is used to ascertain whether or not the heights of students are gamma distributed. From the graphical displays and the chi-squared test results, it is observed that the heights follow gamma distribution even though the maximum likelihood estimates of the parameters are quite influential on the results at 𝛼 ≥ 0.01% significance level.

A New Extension of Quasi Lindley Distribution: Properties and Applications

In this paper, we introduced and studied the statistical properties of a new distribution called the Marshall-Olkin extended quasi Lindley distribution. Specifically, we derived the crude moment, moment generating function, quantile function, and distributions of order statisticsbased on the distribution. The maximum likelihood point estimation method was used to estimate the parameters of the newly introduced model. Some AR minfication processes were discussed. We illustrated the applicability of the distribution using a real dataset.Keywords: Marshal-Olkin family of distributions; maximum likelihood estimates; minification processes; quasi Lindley distribution; quantile function.

Transmuted extended Lomax distribution with some tractability properties and applications

Extending or generalizing original distributions create new distributions with some tractability properties and with more flexibility in modeling data. In this paper, the extended Lomax distribution introduced by Ghitany et al. [1] is further extended in a larger family by introducing an additional parameter. We provide a comprehensive description of the mathematical properties of the subject distribution along with its reliability behavior. The problem of the parameter estimation for the proposed distribution is considered based on the maximum likelihood approach. Finally, the usefulness of the transmuted distribution for modeling reliability data is illustrated using a simulation study and a real data set.