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Eggs are most fundamental and important foodstuff in everyday life for all developed and developing countries. It is not sufficient only to produce and transport eggs at a reasonable cost but arrangements must be made to ensure that the eggs reach the consumers well in time. In between the two extremes of minimization of eggs shipping cost and minimization of eggs shipping time, there exist a number of situations where an eggs transportation system decision maker would like a partial trade-off on shipping cost to attain a certain degree of shipping time advantages. From laying to the final destination, more eggs are broken during shipping than in any other step; therefore, losses due to eggshell breakage are greatest during shipping. In this paper, an algorithm and its underlying theory is developed to solve eggshell breakage restricted bi-criteria eggs transportation problem. This paper discusses a more realistic and general assumption that the eggs shipping time of bi-criteria eggs transportation problem depends on the quantity of the eggs transported and is an increasing piecewise constant function. The algorithm is also supported by a real life eggs shipping problem of Tamil Nadu, India. [ABSTRACT FROM AUTHOR]

This paper aims at proposing a Constraint-based Learning Estimation of Distribution Algorithm (EDA) to solve the Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP). This problem is considered a combinatorial optimization problem and consists of a set of tasks to be carried out in an ordered way, limited availability of resources, and an execution mode for each task. Generally, traditional EDAs handle the constraints of the problem using strategies such as penalty, Pareto dominance, or others, without taking into account these constraints during the learning process. These traditional approaches usually cause the generation of infeasible solutions in each iteration. In this paper, an approach based on the constraints handling during the learning process of the probabilistic model is proposed. To validate the performance of the algorithms, the new approach in two scenarios is compared. First, in the solution of a set of classical optimization problems, where different EDAs are compared in terms of efficiency and efficacy. The second group of experiments is performed on a set of instances of the PSPLIB database. In this case, the proposed algorithm is compared with a traditional EDA and with other results reported in the bibliography. The experimental results demonstrate that the proposal outperforms both the results of traditional EDA and other EDA reported in the literature. [ABSTRACT FROM AUTHOR]

The databases of the Ministry of Public Health of Ecuador in the 2013-2017 period contain valuable information that can be used to determine the strengths, weaknesses, potential problems, among others, that affect the public health of the country. This knowledge can serve to draw better public health policies. This paper aims to propose a methodology that allows us to extract knowledge from these databases and at the same time to obtain association rules based on the combination of algorithms such as FP-growth and k-means. In summary, the methodology consists of the following steps: first, the dataset is stored in 5 files in the SPSS (Statistical Package for the Social Sciences) format, and then the disease-related attributes are grouped and encoded, according to the code ICD-10, for this purpose it is proposed to apply the WEKA software. Finally, the FP-Growth algorithm is used to extract association rules from frequent items with the support of RAPIDMINER, which has the advantage of allowing us the use of WEKA algorithms. The methodology is illustrated with an example that shows how to use it and its usefulness to extract association rules in real-life situations from medical databases. With these representations of the information, morbidity and incidence behavior analysis of the registered groups and diseases can be made. [ABSTRACT FROM AUTHOR]

Multiple Sequence Alignment (MSA) is one of the main topics in the in bioinformatics domain, consists finding an optimal alignment for three or more biological sequences with the number maximum of conserved zones or totally aligned columns. Different scores to assess the quality of the alignments have been proposed, so the problem can be formulated and resolved as a Multi-Objective Optimization Problem (MOP). For this reason, in this paper we present a Multi- Objective approach applied to MSA. We have considered state-of-the-art optimization algorithms aimed at solving different formulations of the MSA: NSGAII, NSGA-III, SPEA2, MOCell, SMS-EMOA, MOEA/D and GWASF-GA. Furthermore we have considered some popular metrics as objectives to be optimized: The weighted Sum-Of-Pairs with a ne gap penalties (wSOP), the Totally Aligned Columns (TC), STRIKE and BaliScore. Finally we have described the main features of our software jMetalMSA, a Multi-Objective optimization software tool applied to MSA problem and illustrated a working example for experimentations purposes. [ABSTRACT FROM AUTHOR]

Proper management of projects has become an element of vital importance for the solution to problems of society. In this context, there are two main objectives to be solved: the construction of project plans which may comply with the constraints of the problem and the optimization in terms of cost and time of those plans. To this end, all activities of project must be organized in such way that the constraints related to the precedence among them and the availability of renewable and non-renewable resources at each instant of time are met. The aim of this paper is to present an Estimation of Distribution Algorithm (EDA), which incorporates the handling of the constraints in the probabilistic model, for the construction of optimal or quasi-optimal project plans. For the validation of the algorithm, authors used both the PSPLib database repository for the development of the scheduling research and the databases of projects of the Repository for Research in Project Management, University of Infomatic Sciences. [ABSTRACT FROM AUTHOR]

Small and Medium Enterprises (SMEs) are important sources of social and economic development in the Latin American region. This paper aims: (1) To study the state of the process of industrialization of cocoa, produced and marketed by PyMES located in the province of Los Ríos, Ecuador, (2) To test the effectiveness of some proposed strategies to overcome the deficiencies found in the process. To achieve these goals, statistical methods were applied to a group of ten SMEs in the region. It was concluded that there exist deficiencies in the process and that the proposed strategies are effective. [ABSTRACT FROM AUTHOR]

The placement of convex objects in the plane, specially rectangular objects has been studied extensively in the literature and its applications to VLSI designs, the steel industry (stock cutting). The placement of non convex objects and its application in many design and textile industries, specially the textile industry has been less studied. Mathematical Approaches determining and adequate solution of such problems include linear programming and the newest genetic algorithms. In this paper we consider a particular industrial problem, the design of printing sheets for decorative porcelain. These objects permit inclusions and other geometric situations which do not appear in the case of convex objects. In this paper we proof results related with this problem for different algorithms: a heuristic algorithm, simulated annealing algorithm and a genetic algorithm. [ABSTRACT FROM AUTHOR]

In this paper three new algorithms for the random generation of permutations of degree n are shown, two of them for the Symmetric group Sn and one for the Alternating group An, in such a way, that they are mainly carried out by means of the operation of additions mod no mod i, i = n..2. [ABSTRACT FROM AUTHOR]

In this paper three new algorithms for the random generation of permutations of degree n are shown, two of them for the Symmetric group Sn and one for the Alternating group An, in such a way, that they are mainly carried out by means of the operation of additions mod no mod i, i = n.2. [ABSTRACT FROM AUTHOR]

In this paper is shown an algorithm about the multiplication of nxn invertibles boolean square matrixes. The algorithm has as novelty that the operations that this algorithm perform are addition and multiplications in the finite field GF(2) and multiplications between polynomials modulo a primitive polynomial, the polynomials belongs to GF(2) [x]. [ABSTRACT FROM AUTHOR]

In the present paper we show a new algorithm for random generation of boolean square invertible matrices nxn. This algorithm has, as initial parameter, a randomly selected boolean matrix A={ai,j}nxn , which has as the only restriction that there is not any I∈ {1…n}, such that ai,i = ai,i+1 = … ai,n = 0. The program is presented in the Mathematic language. [ABSTRACT FROM AUTHOR]

One of the difficulties students from basic courses often show in learning probability and statistics at the university is understanding how the continuous normal probability distribution approximates the discrete binomial probability distribution. In this paper a didactic proposal is presented consisting in two algorithms, very easy to use, developed with software R which is completely free. These algorithms aim to introduce students in basic concepts of normal approximation to the binomial distribution by means of graphical representations and tables. They allow an active participation of student in the learning process. [ABSTRACT FROM AUTHOR]

In many applications the importance of evaluating the consensus is growing. This is particularly important in many biomedical studies where the role of the experts is crucial. That is the case when the efficacy of treatments, the classification of patients, the quality of the biosphere in certain places or tests is studied. This paper is devoted to the development of a review of the κ index, indexes deduced or deducible from it as particular cases under certain models. Properties of them for different models are discussed. The models support parametric inferences or provide a statistical frame for analyzing the consensus in the classification among experts. Different examples are developed with illustrating purposes. [ABSTRACT FROM AUTHOR]

In this paper, we compare, from a theoretical and a practical viewpoint, two settings of a certain tax-programming problem and the algorithms proposed for solving it. This problem may be modeled as a parametric quadratic-programming problem having lower and upper bounds on the variables. The objective function is a quadratic function whose Hessian matrix is diagonal and positive. A term is added that penalizes not to achieving a certain goal. This penalization can be formulated either as an absolute or as a quadratic deviation. [ABSTRACT FROM AUTHOR]