Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class.

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La calidad en el servicio se considera una alternativa para que las empresas puedan obtener una ventaja competitiva y sostenible en un entorno económico globalizado. Las pequeñas y medianas empresas deben ofrecer una mayor calidad en el servicio que las empresas grandes, y así obtener la preferencia de los clientes. El objetivo de este estudio fue identificar la relación entre la variable calidad en el servicio y las variables satisfacción del cliente y lealtad del cliente. Se utilizó el coeficiente de correlación de Spearman y un método estadístico basado en análisis factorial exploratorio que apunta a extraer la varianza máxima del conjunto de datos dentro de cada factor. Los resultados permitieron observar una correlación altamente significativa, positiva y fuerte de la variable de calidad en el servicio con satisfacción del cliente (r = 0.820) y lealtad del cliente (r = 0.803). Un hallazgo importante también fue la asociación entre la dimensión aspectos tangibles con las variables satisfacción del cliente (r = 0.910) y lealtad del cliente (r = 0.919). Por otro lado, en el análisis factorial, a través de la varianza total explicada, se observó que el autovalor es superior a 1 en los cinco primeros casos, donde el porcentaje de la varianza alcanza un valor máximo de 54.886 % en su primer factor. Entonces, con cinco factores se consigue explicar un 73.713 % de la varianza de todos los datos originales. El estudio presentó la limitación de su aplicación en solo una empresa. Se confirmó que a través de una mejor atención y servicio al cliente, la calidad en el servicio constituye una excelente herramienta para la rentabilidad y sostenibilidad de la empresa.

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Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.