The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision‐making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

The binarization of Swarm intelligence continuous metaheuristics is an area of great interest in operations research . This interest is mainly due to the application of binarized metaheuristics to combinatorial problems . In this article we propose a general binarization algorithm called K-means Transition Algorithm (KMTA). KMTA uses K-means clustering technique as learning strategy to perform the binarization process. In particular we apply this mechanism to Cuckoo Search and Black Hole metaheuristics to solve the Set Covering Problem (SCP). A methodology is developed to perform the tuning of parameters. We provide necessary experiments to investigate the role of key ingredients of the algorithm. In addition, with the intention of evaluating the behavior of the binarizations while the algorithms are executed, we use the Page's trend test. Finally to demonstrate the efficiency of our proposal, Set Covering benchmark instances of the literature show that KMTA competes clearly with the state-of-the-art algorithms.

Despite the importance of the role of small and medium enterprises (SMEs) in developing and growing economies, little is known regarding the use of management control tools in them. In management control in SMEs, a holistic system needs to be modeled to enable a careful study of how each lever (belief systems, boundary systems, interactive control systems, and diagnostic control systems) affects the organizational performance of SMEs. In this article, a fuzzy logic approach is proposed for the decision-making system in management control in small and medium enterprises. C. Mamdani fuzzy inference system (MFIS) was applied as a decision-making technique to explore the influence of the use of management control tools on the organizational performance of SMEs. Perceptions data analysis is obtained through empirical research.

Motivated by the fact that Chile is one of the most seismically active countries in the world (located over the 'Pacific Ring of Fire'), we define a methodology for estimating the cost of housing reconstruction by modelling the occurrence of natural disasters as a Markov chain.Specifically, the states of the chain correspond to the different possible conditions of the housing infrastructure and the transition probabilities represent the possibility of change from one condition to another once the disaster has occurred.We prove that for the case of the 2010 Chilean earthquake, the matrix representing the process admits a stationary state vector.Using this vector, which we interpreted as the portion of time that the chain spends in each state in the long term, we define a cost function associated with total reconstruction.If this cost function is continuous, then this methodology allows policymakers to make decisions when facing the trade-off between current partial reconstruction and future total reconstruction.

Context: In the industry the resources are increasingly scarce. For this reason, we must make a gooduse of it. Being the optimization tools, a good alternative that it is necessary to bear in mind. A realworldproblem is the facilities location being the Set Covering Problem, one of the most used models.Our interest, it is to find solution alternatives to this problem of the real-world using metaheuristics. Method: One of the main problems which we turn out to be faced on having used metaheuristic is thedifficulty of realizing a correct parametrization with the purpose to find good solutions. This is not aneasy task, for which our proposal is to use a metaheuristic that allows to provide good parameters toanother metaheuristics that will be responsible for resolving the Set Covering Problem. Results: To prove our proposal, we use the set of 65 instances of OR-Library which also was comparedwith other recent algorithms, used to solve the Set Covering Problem. Conclusions: Our proposal has proved to be very effective able to produce solutions of good qualityavoiding also have to invest large amounts of time in the parametrization of the metaheuristic responsiblefor resolving the problem.

Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State–Action–Reward–State–Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.

In recent years, information has become one of the most important goods for organizations as it brings insights about customer preferences and internal processes that could help to improve organizational performance, decrease costs, improve customer engagement, among other benefits. The consumption of information within an organization has been studied in the literature under several approaches associated to information system success nor information management, but the Information Consumer eXperience (ICX) has not been evaluated following a formally defined methodology. In this work, a methodology to formalize the ICX evaluation process within the organization is proposed. The main goal of this methodology is to improve ICX into the organization by generating recommendations based on information consumers perceptions under a customer experience CX approach. The proposed methodology consists of 3 sequential stages: Characterization, Experimentation and Analysis. In The Characterization Stage an exploratory diagnosis is performed, including the experimental setup planification, consumers behavior exploration, and a preliminary version of the customer Journey Map. The Experimentation Stage is focused on data collection using different instruments such as surveys, interviews, questionnaire, and a mixed qualitative and quantitative instrument to generate data about consumers expectations and perceptions. In the third stage of Analysis, the collected data is analyzed to generate a definitive Customer Journey Map, through quantitative and qualitative data analysis. Our proposed ICX evaluation methodology is the first formally described methodology for information consumer perceptions analysis and experience evaluation. Which could be used to face ICX analysis into any kind of organization that works with information.

The purpose of this study was to identify and measure the impact of the different effects of entropy states over the high-frequency trade of the cryptocurrency market, especially in Bitcoin, using and selecting optimal parameters of the Bayesian approach, specifically through approximate Bayesian computation (ABC). ABC corresponds to a class of computational methods rooted in Bayesian statistics that could be used to estimate the posterior distributions of model parameters. For this research, ABC was applied to estimate the daily prices of the Bitcoin cryptocurrency from May 2013 to December 2021. The findings suggest that the behaviour of the parameters for our tested trading algorithms, in which sudden jumps are observed, can be interpreted as changes in states of the generated time series. Additionally, it is possible to identify and model the effects of the COVID-19 pandemic on the series analysed in the research. Finally, the main contribution of this research is that we have characterised the relationship between entropy and the evolution of parameters defining the optimal selection of trading algorithms in the financial industry.

The purpose of this paper is to identify and measure the impact of the variables affecting the increase in the number of patents as a way to advance the development of policies in countries in terms of sustainable development based on innovation. An econometric estimation of a mixed model was used to measure the impact of patent development on the countries analyzed in this research. The findings suggest that economies that have some relevance in research and development have increasing numbers of patents. Thus, the empirical findings relate to the theoretical models that state that comparative advantages may be dynamic due to technological innovation. Finally, this paper shows that innovation is a central parameter to engage in research and develop a knowledge-based economy.

En 2014, un grupo de jóvenes diputados propuso reducir en un 50% la dieta parlamentaria en Chile. Con esta iniciativa se buscaba solucionar problemas de legitimidad derivados de los altos ingresos de los congresistas en un contexto de alta desigualdad. La literatura, generalmente, señala que la dieta parlamentaria es un requisito para la ampliación democrática, pues permite que personas que no tienen otros ingresos (e.g., rentas o inversiones) que los provenientes del trabajo diario puedan ejercer la función representativa en el Congreso. Sin embargo, la ciencia política no se ha detenido a estudiar en detalle que la dieta parlamentaria no es siempre vista como un instrumento capaz de mejorar la calidad de la representación política, sino a veces como una fuente de conflicto político, especialmente cuando se trata de magnitud relativa. Este artículo busca analizar esta arista, a partir de un estudio de los debates que la dieta, y especialmente su monto, ha generado en la sociedad chilena entre 1924 y 2021. Se sostiene que, en un plazo de 90 años, la dieta pasó de ser considerada como un instrumento útil para incorporar nuevos sectores sociales a la vida política a ser visto como uno que genera una distorsión de la representación al dotar a los parlamentarios de un nivel de vida que los coloca dentro de una muy selecta minoría de personas con altos ingresos.

El envejecimiento poblacional a nivel global y local es una realidad que desafía a todas las disciplinas, incluyendo al diseño. La Región de Valparaíso posee la mayor proporción de personas ≥ de 60 años o Adultos Mayores (AM) del país. Entre las situacionesproblema que enfrenta este grupo poblacional, en especial aquellos que se encuentran en un entorno social con múltiples carencias y vulnerabilidades, está la malnutrición. Chile cuentacon un programa de alimentación complementaria orientado a los AM, sin embargo, no ha dado el resultado esperado. Este artículo describe y analiza una experiencia de diseño llevada a cabo en Valparaíso, en el contexto de un proyecto social que busca mejorar la eficacia del programa público de alimentación. La experiencia, metodológicamente, consistió en el diseño participativo de un sistema de preparaciones alimentarias saludables paraadultos mayores autovalentes, contemplando, por una parte, la caracterización sociodemográfica, de salud, hábitos y preferencias alimentarias de un grupo de adultos mayores autovalentes y vulnerables socioeconómicamente y, por otra, la realización de talleres de diseño participativo, para construir colaborativamente el sistema de comunicación del proyecto: marca y etiqueta de los alimentos. Se entregaron, además, preparaciones alimentariaspara ser degustadas y valoradas por los AM durante tres meses. La participación fue voluntaria con firma de consentimiento informado. Resultados: los atributos positivos de la marca y las etiquetas que más destacan los AM son los siguientes: el diseño novedoso, el nombre, los dibujos, los colores llamativos; complementariamente, los aspectos que se debieran ajustar, desde la perspectiva de los AM, son: letras más grandes y claras. Desde el análisis de la experiencia se identifican tres factores críticos para este nivel de diseño social: 1. El rol del diseñador en un “modelo social” de intervención profesional, 2. El rol del usuario-participante en el proceso de co-diseño, y 3. El rol del diseño en el contexto social.

The estuaries of the Aconcagua and Maipo Rivers of central Chile are receptors of residues that contain metals from anthropic activities including agriculture, mining and smelters, which have different levels in the two basins. This study postulates that the exposition to metals is different in the two estuaries and that their sediments contain bioavailable chemical agents that produce oxidative stress. The aim of the study was to evaluate the effect of estuarine sediments on the polychaete Perinereis gualpensis using oxidative stress biomarkers and to determine the metal concentrations in sediments and their accumulation in P. gualpensis . Sediments and organisms were collected in December 2015 and January 2016 in the estuaries. The Catapilco estuary was used as control, since its basin has little anthropic activity. The metal concentrations of Fe Cu, Pb, Zn and Cd were determined in tissues of the organisms and in sediments. The granulometry, conductivity, redox potential, pH and organic matter in sediments were determined, as well as catalase activity and lipid peroxidation. The results show that the concentrations of metals in sediments were higher in the estuary of the Aconcagua River: Cu: 48 ± 2 μg g −1 ; Fe: 154 ± 19 mg g −1 , Pb: 20 ± 3 μg g −1 and Zn: 143 ± 20 μg g −1 . In tissues, Pb and Fe were higher in the estuary of the Maipo River, while Cd was detected only in the Catapilco River mouth. Catalase activity was greater in the estuary of the Aconcagua River and lipid peroxidation in the estuary of the Catapilco River. Significant regressions were found between biomarkers of oxidative stress and metal concentrations in tissues of P. gualpensis . In conclusion, the sediments of the studied estuaries contain bioavailable chemical agents that provoke oxidative stress in P. gualpensis , which may be a risk for the benthic communities of these ecosystems. This species is proposed to monitor metals bioavailability and oxidative stress in estuarine sediments.

The digitization of information and technological advancements have enabled us to gather vast amounts of data from various domains, including but not limited to medicine, commerce, and mining. Machine learning techniques use this information to improve decision-making, but they have a big problem: they are very sensitive to data variation, so it is necessary to clean them to remove irrelevant and redundant information. This removal of information is known as the Feature Selection Problem. This work presents the Pendulum Search Algorithm applied to solve the Feature Selection Problem. As the Pendulum Search Algorithm is a metaheuristic designed for continuous optimization problems, a binarization process is performed using the Two-Step Technique. Preliminary results indicate that our proposal obtains competitive results when compared to other metaheuristics extracted from the literature, solving well-known benchmarks.