Search

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]

In this paper, authors consider optimal replenishing strategies for constant demand in various financial scenarios by considering non-instantaneous deteriorating item under the progressive trade credit policy. The aim of this work is to develop a cost function for various situations depending on the trade credit period in economic environment. An algorithm is established to obtain the average cost, the replenishment time and the optimal order quantity. A thorough sensitivity analysis was carried out to assess the importance of the model. [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]

This paper deals with approximate solutions of an optimization problem with interval-valued objective function. Four types of approximate solution concepts of the problem are proposed by considering the partial ordering LU on the set of all closed and bounded intervals. We show that these solutions exist under very weak conditions. Under suitable constraint qualifications, we derive Karush-Kuhn-Tucker necessary and sufficient optimality conditions for convex interval-valued optimization problems. [ABSTRACT FROM AUTHOR]

This review paper systematizes a detailed, didactical and own construction of the singular value decomposition (SVD). The theory is complemented with relevant applications to image processing. Also, the algorithms of those applications and numerical examples are shown. The way how the document is organized allows it to be useful for students and researchers with interest in the SVD and its applications to image processing. [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]

This research is based on the Derivative/Integration of the Power Spectrum for the reduction of noise in a periodic signal. The performed procedure, joined to a convolution process, results in the detection of the original values of amplitude, phase and frequency of the spectral components of the periodic signal. In this paper, second-order statistics foundations are presented and the validation of the proposed algorithm is exposed in both theoretical and practical sense. [ABSTRACT FROM AUTHOR]

In this paper, we asses the efficiency of Brazilian airports, based on Data Envelopment Analysis (DEA). Although previous studies applied DEA to Brazilian airports, herein we consider DEA´s homogeneity assumption, according to which units are assumed to operate in similar environments. As airport environment differs significantly throughout the country, we limit our analysis to six central airports. Still, our results are valuable nationwide, because these airports handle half of all flights departing from Brazil. As classic DEA is not recommended for small data sets, we use Multiple Criteria DEA, which has more discriminatory power. In our study, we found that only two airports in the State of São Paulo are efficient, whereas the other central airports are under-utilized, with an efficiency score of less than 50%. Results found in this study could serve as an important tool for public politics, as well as for private decisions in the airport sector. [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]

In this paper the minimization of a non-convex quadratic function under box constraints is considered. For solving this problem, we propose a hybrid strategy combining a cut and continuation method with a Branch and Bound procedure, based on double non-negative relaxation. We compare the results of the new approach with the Branch and Bound method and with the preceding hybrid algorithm. The influence of characteristics of the problem in the algorithm's behavior is explored. Computational experiments include problems of higher dimension than the ones reported in the referred literature and show that the new strategy improves the results concern to number of explored nodes and computation time. [ABSTRACT FROM AUTHOR]

In recent years, the development of multi-classifier systems has become an active research field. A multi-classifier system is an ensemble of classification algorithms whose individual outputs are fused together for better accuracy and interpretability. An important aspect when designing such systems is related to the heterogeneity of the building blocks (classifiers) that make up the ensemble, since previous studies have uncovered that a more diverse ensemble often boosts up the overall classification power. Some statistical measures can be used to estimate how diverse the classifier ensembles are; they are called diversity measures. Another issue to be considered is the number of individual classifiers included in the model: the lower the number of classifiers, the simpler the resulting system. In general terms, the parsimony principle is highly desired in such ensembles, since a bulky ensemble will also be a very time-consuming model. Finding the minimal subset of individual classifiers that brings about the best system performance can be posed as a combinatorial optimization problem. In this paper, we address the problem of building multi-classifiers systems from the perspective of Ant Colony Optimization (ACO), a widely popular and effective metaheuristic optimization algorithm. The main reason behind the use of ACO lies on its strong ability to solve entangled combinatorial optimization problems. An empirical analysis is included to statistically validate the benefits of our proposal. [ABSTRACT FROM AUTHOR]

Taking the clue from the pioneer work of Tarray and Singh (2015) we have suggested a new stratified randomized response model. In this paper the problem of optimal allocation in stratified random sampling where randomized response technique is used in presence of non response. The problem is formulated as a Nonlinear Programming Problem (NLPP) and is solved using Branch and Bound method. Also the results are formulated through LINGO. [ABSTRACT FROM AUTHOR]

Many real-life scenarios can be modeled as Dynamic Optimization Problems (DOPs), which demand for finding optimal solution over time. From the viewpoint of metaheuristics methods, DOPs have been extensively addressed over the last two decades. One important issue in this context is how to assess the algorithm performance. Most of current proposals rely on single information from data, which limits the notion about the overall performance of the algorithm. So, in order to contribute to this issue, in this paper we propose a new performance measure for algorithm assessment in evolutionary dynamic optimization. We derived our proposal from what we considered as effectiveness in dynamic environments. Different from other existing measures, our proposal involve not only the accuracy, but also the time (efficiency) of the algorithm. In order to illustrate its usefulness and relationship with other literature measures an experimental analysis was conducted. Results show that the proposed measure can be suitable employed in typical experimentation scenarios and offers new information about the algorithms performance. [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]