Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Time series play a major role in a large number of economic problems such as predicting the volatility of price indices and market rates on the stock exchanges. Volatility reflects the behavior of fluctuations in the prices of assets, allowing to measure the risk of portfolios. However, volatility is not directly observable, is not constant throughout the period, is dependent on the past and does not follow Gaussian normality. In the literature, there have been many studies using classic methods for the Mexican Stock Exchange. New heuristic methods of supervised learning for forecasting financial time series are presented in this paper. These methods are structured in such a way that, knowing only the time series, the values of the dependent variable several periods ahead are estimated with low errors. This work presents a hybrid method based on support vector machines, fuzzy controllers, evolutionary algorithms and classic methods applied to the asset portfolios optimization of the Mexican Stock Exchange. New heuristic methods of supervised learning for forecasting financial time series are presented in this paper. Besides, a fuzzy logic approach with a Genetic Support Vector Regression (Fuzzy GA-SVR) algorithm for the prediction of time series with a broad spectrum of application is presented. This algorithm uses an online tuning process to produce superior efficacy than the classic forecasting methods. Experimentation presented in the paper shows that Fuzzy GA-SVR outperforms both, the classic ARIMA methods and the individual use of SVR.

The exponential expansion of sentiment analysis is mainly due to the interest of companies to obtainment users’ views about products and services. Likewise, sentiment analysis has been widely and successfully used in publicity and selling strategies as well as behaviour patterns and user preferences identification. In the same way, has been applied to educational areas to increase the learning, and e-learning quality through the mining analysis data on teaching performance assessment or student’s comments. This work describes the development and evaluating the process of a Model called “SocialMining“, which is focused on higher education with the purpose of improve teaching techniques of teachers and recommend courses for teacher improvement, through teacher performance evaluation made by students comments. The SocialMining model analyze students’ comments by means representative machine learning algorithms such as: Support Vector Machines and Random Forest. We applied metrics as evaluation measure to measuring the performance of algorithms. Finally, we have implemented this model in Universidad Politécnica de Aguascalientes (Mexico) and the results obtained show that it is feasible to perform sentiment analysis to classify comments of Teacher Performance Assessment using Machine Learning with high accuracy (85%).

The Project Scheduling Problem (PSP) is a generic name given to a whole class of problems in which the best form, time, resources and costs for project scheduling are necessary. The PSP is an application area related to the project management. This paper aims at being a guide to understand PSP by presenting a survey of the general parameters of PSP: the Resources (those elements that realize the activities of a project), and the Activities (set of operations or own tasks of a person or organization); the mathematical models of the main variants of PSP and the algorithms used to solve the variants of the PSP. The project scheduling is an important task in project management. This paper contains mathematical models, resources, activities, and algorithms of project scheduling problems. The project scheduling problem has attracted researchers of the automotive industry, steel manufacturer, medical research, pharmaceutical research, telecommunication, industry, aviation industry, development of the software, manufacturing management, innovation and technology management, construction industry, government project management, financial services, machine scheduling, transportation management, and others. The project managers need to finish a project with the minimum cost and the maximum quality.

The behavior of stock exchanges around the world has an important role in the financial development of countries. The Mexican Stock Exchange (BMV, for its acronym in Spanish) is the financial entity in Mexico where investors build and manage investment portfolios to generate profit. Financial time series forecasting is an important problem for investment portfolios creation and optimization because it allows them to have a prediction of the value of investment assets over time, therefore reducing at some extent the uncertainty of these operations. Classical models, such as ARIMA, artificial neural networks and support vector machines, are usually used as forecasting tools. In this paper, we forecast financial time series of the BMV using a hybrid parallel algorithm that employs both simulated annealing (SA) and support vector machines for regression (SVR). Finally, the resulting Mean Absolute Percent Error (MAPE) of this hybrid algorithm is compared with that of an ARIMA model.

During recent years coastal flooding in Mexico has increased, due to this situation, it is necessary to have alternative ways to serve the affected population. Government (G), non-governmental organizations (NGOs), civil society organizations (CSOs) and private enterprises (PEs) are the main players involved in humanitarian aid. This aid in Mexico comes through national and international instances. Here the actors meet the needs of the people affected individually and not collectively. Pointing out that sometimes the same population (P) provides help to themselves. This research intends in a qualitative way to help the Mexican government to incorporate the integration of third and fourth-party logistics as actors for humanitarian aid; by proposing the formation of providers’ groups focused on humanitarian aid with the aforementioned actors. This would help the population more efficiently by applying horizontal and vertical coordination, to reduce the response time when the affected population is in danger.

High levels of consumption of electrical and electronic equipment, together with the decline of their useful time of life (obsolescence), has led to a progressive growth in the generation of  Waste Electrical and Electronic Equipment (WEEE), also known as e-waste. The problems involving inadequate management of these are reflected mainly in the negative impact on the environment, but it also entails a waste of opportunities for reuse of components that can still generate value. This article presents a review of the literature on the studies that address the appropriate process of recovery of WEEE, i.e., the process of Reverse Logistics, and identifies the trends of study on the subject and proposed new lines of research in the management of the WEEE.  

In this paper was raised as support to the rescue of the indigenous language called hñähñu (Otomí) existing in Mexico, in order to further develop the learning of this language in children's basic level, providing a teaching tool different from usual that children interact. The UNESCO marks that the countries should cultivate three languages: a native or local, in this case is hñähñu, or a national which is the Spanish, and the third is the implementation with other international language (English). The intelligent tutor system via web based on Joomla and PHP programming for learning the hñähñu or Otomí to the new generations and thus rescue the language from extinction. This research originates from an extensive and exhaustive collection of information from different bibliographic sources until the construction of intelligent tutoring system. The research work is designed to teach the basis of the Otomi language which is focused on the children of basic level in the region of the Mezquital Valley which is located in the State of Hidalgo, Mexico.

This paper addresses the project portfolio selection problem enriched with scheduling. The factors considered for project scheduling are: the planning time horizon and the negative impact of the project completion time on the total profit. The objective is to select a subset of projects which maximizes the discounted total gain by late completion time, respecting resource constraints and without exceeding the time horizon. A mixed integer linear programming model was formulated and compared to two recent models addressing the same problem. To show the potential of the model on the large scale we used a metaheuristic based on the genetic algorithm Non-dominated Sorting Genetic Algorithm II. We show experimentally the benefits of our proposal and leave open the possibility of its study applied on a larger scale in future works.

This article presents a methodology for solving the Periodic Vehicle Routing Problem (PVRP) with an Iterated Local Search Metaheuristic (ILS). The problem is solved in two phases: the first step is to assign days of visit to each customer, and in the second step to determine the routes that each vehicle must perform each day. The heuristic for local improvement in ILS is Clarke & Wright Heuristic, and perturbation is made on the days of visit assigned to some customers. The instances generated by Cordeau for PVRP with 51, 102 and 153 customers are used. The results are compared to the best known solutions. The gap between the results presented by the proposed metaheuristic range from 15% to 5% above the best known solutions. The time to find the solutions with the proposed metaheuristic goes from 6.76 seconds for instances of 51customers, to 172.09 seconds for instances of 153 customers.