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.

The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.

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.

In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.

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.

In this work, a new optimization method inspired on the Thermodynamic Equilibrium is described to address nonlinear problems in continuous domains. In our proposal, each decision variable is treated as the most volatile chemical component of a saturated binary liquid mixture at a determined pressure and temperature. The optimization procedure is started with an initial solution randomly generated. The search is done by changing the equilibrium state of each mixture. The search is carried out by accepting worse solutions to avoid being left trapped in local optimums. The search includes the random change of the mixtures. The algorithm was tested by using known mathematical functions as benchmark functions showing competitive results in comparison with other metaheuristics.

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.

The binarization of continuous swarm-intelligence metaheuristics is an area of great interest in operational 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 Percentil Transition Ranking Algorithm (PTRA). PTRA uses the percentile concept as a binarization mechanism. In particular we apply this mechanism to the Cuckoo Search metaheuristic to solve the Set Covering Problem (SCP). We provide necessary experiments to investigate the role of key ingredients of the algorithm. Finally to demonstrate the efficiency of our proposal, Set Covering benchmark instances of the literature show that PTRA competes with the state-of-the-art algorithms.

The binarization of Swarm Intelligence continuous metaheuristics is an area of great interest in operational 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 Percentile Transition Ranking Algorithm (PTRA). PTRA uses the percentile concept as a binarization mechanism. In particular we will apply this mechanism to the Cuckoo Search metaheuristic to solve the set multidimensional Knapsack problem (MKP). We provide necessary experiments to investigate the role of key ingredients of the algorithm. Finally to demonstrate the efficiency of our proposal, we solve Knapsack benchmark instances of the literature. These instances show PTRA competes with the state-of-the-art algorithms.

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 need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature.

Prior research on the successful design and construction of data visualization systems or information management systems has not fully taken into account the holistic experience of employees working with information within the organization but has centered on specific aspects, such as user experience or data quality, attempting to go against information management quality, as those approaches can significantly influence users’ perceptions and their motivation to effectively use such tools for decision making. This study addresses the information consumer experience (ICX) in a Chilean Higher Education institution through the design and implementation of an user-centric centralized information management system. This system was created using an adapted design thinking methodology with an ICX perspective to identify and integrate the information consumers’ demands and other factors correlated with ICX into the system’s design. The proposed system is a technological extension of an information resource validation process that involves senior data analysts from the Analytics Department and external data analysts from other departments across the organization. This process helps to address data quality and information management quality (IMQ) problems of the organization, representing a centralized data source for all information consumers into the organization, offering consistent, accessible, and good quality data to address daily work and enhance information consumers experience, and managerial work.

The Information Consumer Experience (ICX) significantly impacts organizational performance. ICX is influenced by three key dimensions: personal, social, and organizational. However, no studies have provided a solid theoretical foundation for ICX. This study presents a theoretical model that integrates these dimensions within the Theory of Motivated Information Management (TMIM) to offer a comprehensive framework for understanding and analyzing ICX in organizations. The proposed model combines personal, social, and organizational factors within the TMIM framework, providing a holistic view of how information consumption affects employee performance and organizational outcomes. It emphasizes individual cognitive and emotional responses, as well as the broader organizational context in which information is managed and shared. Our findings show that incorporating these dimensions into the TMIM framework strengthens the model, addressing TMIM’s limitations and providing a more robust approach to ICX. Specifically, the inclusion of emotional factors beyond anxiety, the role of social interactions and organizational culture, and the impact of organizational structures and technology policies enriches the model. These findings suggest that optimizing information consumption through improved management can enhance organizational efficiency, employee satisfaction, and overall performance. This model fills a gap in the literature, offering a theoretical basis for future ICX research and empirical exploration of the interaction between these dimensions in organizational contexts.

Las redes sociales han contribuido a una nueva estrategia de marketing donde las marcas forjan alianzas con los influencers de las redes sociales para generar contenido publicitario. Esta estrategia conocida como marketing de influencer se encuentra en crecimiento y ganando un interés sustancial en la literatura. Sin embargo, la comprensión actual de la percepción de los influencers de las redes sociales frente a la transparencia del contenido que publicitan sigue siendo limitada, donde prácticamente todos los estudios se centran en la percepción del seguidor y en países desarrollados donde existe mayor regulación frente a la transparencia publicitaria. Para abordar esta problemática, la presente investigación utilizó la teoría de credibilidad de la fuente y la teoría institucional con el objetivo de explorar las percepciones de los influencers de las redes sociales frente a la transparencia del contenido que publicitan. Sobre la base de un estudio cualitativo que incorporó entrevistas semiestructuradas con ocho influencers de las redes sociales en Chile. Los resultados de este estudio muestran que los influencers de las redes sociales consideran relevante en su credibilidad de la transparencia publicitaria; la confiabilidad, la experiencia, la auto-presentación en línea y los aspectos normativos. Estos hallazgos contribuyen a la literatura de marketing de influencer y también tiene importantes implicancias prácticas para el amplio y creciente campo de la publicidad como son los profesionales de marketing, las marcas y los agentes reguladores encargados de proteger al consumidor.

In the context of a private organization, this study focuses on the implementation of an agile methodology for assessing Information Consumer Experience (ICX). The objective of this research is to diagnose and apply a simplified version of the methodology within the organizational setting. The proposed agile methodology offers a rapid approach to evaluate ICX in smaller organizations or departments within the organization, intending to enhance it through the generation of recommendations based on information consumer perceptions. This approach centers on Customer Experience (CX). The methodology is divided into three sequential stages: Characterization, Experimentation, and Analysis. In the Characterization stage, information consumers, providers, and the products, systems, or services delivering information are identified. The Experimentation stage focuses on data collection, employing various information collection instruments, as well as both qualitative and quantitative approaches to gather insights into consumer expectations and perceptions. The third stage, Analysis, involves the processing and analysis of the collected data, using both quantitative and qualitative methods to integrate the findings. This study introduces an innovative methodology for evaluating and enhancing the Information Consumer Experience in any organization that manages data and information. The results obtained in this research provide guidance for improving ICX within the organization under study and serve as a resource for future research in the fields of information management and customer experience.

Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.