Comentario de Libros: Planificación de turnos en un aeropuerto: Uso de simulación y metaheurísticos.

Este trabajo estudia el problema de la planificación de los turnos del personal de un aeropuerto encargado de los controles de seguridad y de los mostradores de facturación, que son los puntos estratégicos que más van a incidir en la fluidez de los pasajeros. Concretamente se trata de racionalizar los costes de este personal, a la vez que se garantiza un grado de fluidez mínima en cada uno de estos puntos, los cuales pueden ser modificados por los órganos rectores del aeropuerto, de acuerdo a sus preferencias

Variable neighborhood search approach to face‐shield delivery during pandemic periods

In 2020, the COVID-19 pandemic and its rapid spread shook health authorities worldwide at the regional and national levels. Healthcare systems had difficulty acquiring important supplies, such as face shields, which at that time were essential for healthcare staff. The need for this material increased with the spread of the pandemic. In most areas, warehouses did not have a sufficient stock of this product. This situation has occurred in the cities and provinces of Burgos (Spain). Volunteers (citizens and small companies) owning three-dimensional printers offered themselves to manufacture face shields. These volunteers are called “makers.” Similarly, different organizations (mainly Civil Protection) took charge of transport activities (delivery of material to the makers, collection of face shields, and delivery of the latter to hospitals and other entities). In this study, we were tasked with developing a system for planning and rationalizing these activities. The problems that were solved included a vehicle routing problem with different characteristics compared with other models in the literature. A previuous work described this problem, and the heuristic method used for the planning. However, it is necessary to develop tools that are as efficient as possible for similar situations. In this study, we propose a mathematical formulation of the problem and a method based on the metaheuristic strategies variable neighborhood search and greedy randomize adaptative search procedure on a multistart framework. Different tests with real instances used during the period in which these activities were conducted show that the new method improves the results obtained by the previous method as well as the commercial software.The authors are grateful to the following entities and projects: The Spanish Research Agency (Projects PID2019-104263RB-C44, PDC2021–121021-C22, and PID2022-139543OB-C44) and the Regional Government of “Castilla y León” and FEDER funds (Project BU056P20)

Diseño de un sistema para la resolución del problema de programación de turnos en un aeropuerto.

En este trabajo se desarrolla un sistema para la resolución del problema de programación de horarios de trabajo en un modelo de flujo de pasajeros en un aeropuerto. Concretamente se trata de racionalizar los costes del personal de facturación y de seguridad a la vez que se garantiza un nivel de fluidez mínimo requerido en el tránsito de los pasajeros, medido en términos de los tiempos de espera. El sistema está compuesto por tres herramientas principales: un simulador, un método para optimizar simulaciones y un método para resolver el problema de programación de horarios propiamente dicho. Este sistema permite la obtención de una gran diversidad de soluciones facilitando así la tarea del decisor que puede seleccionar la alternativa más adecuada dependiendo de sus preferencias. Las soluciones generadas se aproximan a la curva de eficiencia teniendo en cuenta los objetivos de coste de personal y nivel de servicio ofrecido

Variable selection for linear regression in large databases: exact methods

This paper analyzes the variable selection problem in the context of Linear Regression for large databases. The problem consists of selecting a small subset of independent variables that can perform the prediction task optimally. This problem has a wide range of applications. One important type of application is the design of composite indicators in various areas (sociology and economics, for example). Other important applications of variable selection in linear regression can be found in fields such as chemometrics, genetics, and climate prediction, among many others. For this problem, we propose a Branch & Bound method. This is an exact method and therefore guarantees optimal solutions. We also provide strategies that enable this method to be applied in very large databases (with hundreds of thousands of cases) in a moderate computation time. A series of computational experiments shows that our method performs well compared to well-known methods in the literature and with commercial software.This work was partially supported by FEDER funds and the Spanish Ministry of Economy and Competitiveness (Projects ECO2016-76567-C4-2-R and PID2019-104263RB-C44), the Regional Government of “Castilla y León”, Spain (Project BU329U14 and BU071G19), the Regional Government of “Castilla y León” and FEDER funds (Project BU062U16 and COV2000375)

A stepped tabu search method for the clique partitioning problem

Given an undirected graph, a clique is a subset of vertices in which the induced subgraph is complete; that is, all pairs of vertices of this subset are adjacent. Clique problems in graphs are very important due to their numerous applications. One of these problems is the clique partitioning problem (CPP), which consists of dividing the set of vertices of a graph into the smallest number of cliques possible. The CPP is an NP-hard problem with many application fields (timetabling, manufacturing, scheduling, telecommunications, etc.). Despite its great applicability, few recent studies have focused on proposing specific resolution methods for the CPP. This article presents a resolution method that combines multistart strategies with tabu search. The most novel characteristic of our method is that it allows unfeasible solutions to be visited, which facilitates exploration of the solution space. The computational tests show that our method performs better than previous methods proposed for this problem. In fact, our method strictly improves the results of these methods in most of the instances considered while requiring less computation time.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by FEDER funds and the Spanish State Research Agency (Projects PID2019-104263RB-C44 and PDC2021–121021-C22); the Regional Government of “Castilla y León”, Spain (Project BU071G19); the Regional Government of “Castilla y León”; and FEDER funds (Project BU056P20)

Predicción de la quiebra empresarial: el modelo GRASP-LOGIT

URL del artículo en la web de la Revista: https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2810Predicting corporate failure is an important problem in management science. This study tests a new method for predicting corporate failure on a sample of Spanish firms. A GRASP (Greedy Randomized Adaptive Search Procedure) strategy is proposed to use a feature selection algorithm to select a subset of available financial ratios, as a preliminary step in estimating a model of logistic regression for predicting corporate failure. Selecting only a subset of variables (financial ratios) reduces the costs of data acquisition, increases prediction accuracy by excluding irrelevant variables, and provides insight into the nature of the prediction problem allowing a better understanding of the final classification model. The proposed algorithm, that it is named GRASP-LOGIT algorithm, performs better than a simple logistic regression in that it reaches the same level of forecasting ability with fewer accounting ratios, leading to a better interpretation of the model and therefore to a better understanding of the failure process.La predicción de la quiebra empresarial es un problema que goza de una gran relevancia en las ciencias empresariales. En este trabajo se propone un nuevo método para predecir la quiebra empresarial en una muestra de empresas españolas. Concretamente se trata de un algoritmo de selección de variables basado en la estrategia metaheurística GRASP (procedimiento de búsqueda adaptativa aleatoria y voraz) para seleccionar un subconjunto de ratios financieros, como un paso preliminar para estimar un modelo de regresión logística que prediga la quiebra empresarial. La selección de un subconjunto de ratios financieros, de entre todos los disponibles, reduce los costes de adquisición de datos, aumenta la precisión de la predicción al excluir las variables irrelevantes y proporciona información sobre la naturaleza del problema de predicción. Todo lo anterior permite una mejor comprensión del modelo de clasificación final. Nuestro nuevo modelo, al que llamamos modelo GRASP-LOGIT, funciona mejor que una simple regresión logística en el sentido de que alcanza el mismo nivel de capacidad de predicción con menos ratios contables, lo que lleva a una mejor interpretación del modelo y, por lo tanto, a una mejor comprensión del proceso de quiebra empresarial.Universidad Pablo de Olavid

Predicting Corporate Failure: The GRASP-LOGIT Model || Predicción de la quiebra empresarial: el modelo GRASP-LOGIT

Predicting corporate failure is an important problem in management science. This study tests a new method for predicting corporate failure on a sample of Spanish firms. A GRASP (Greedy Randomized Adaptive Search Procedure) strategy is proposed to use a feature selection algorithm to select a subset of available financial ratios, as a preliminary step in estimating a model of logistic regression for predicting corporate failure. Selecting only a subset of variables (financial ratios) reduces the costs of data acquisition, increases prediction accuracy by excluding irrelevant variables, and provides insight into the nature of the prediction problem allowing a better understanding of the final classification model. The proposed algorithm, that it is named GRASP-LOGIT algorithm, performs better than a simple logistic regression in that it reaches the same level of forecasting ability with fewer accounting ratios, leading to a better interpretation of the model and therefore to a better understanding of the failure process. || La predicción de la quiebra empresarial es un problema que goza de una gran relevancia en las ciencias empresariales. En este trabajo se propone un nuevo método para predecir la quiebra empresarial en una muestra de empresas españolas. Concretamente se trata de un algoritmo de selección de variables basado en la estrategia metaheurística GRASP (procedimiento de búsqueda adaptativa aleatoria y voraz) para seleccionar un subconjunto de ratios financieros, como un paso preliminar para estimar un modelo de regresión logística que prediga la quiebra empresarial. La selección de un subconjunto de ratios financieros, de entre todos los disponibles, reduce los costes de adquisición de datos, aumenta la precisión de la predicción al excluir las variables irrelevantes y proporciona información sobre la naturaleza del problema de predicción. Todo lo anterior permite una mejor comprensión del modelo de clasificación final. Nuestro nuevo modelo, al que llamamos modelo GRASP-LOGIT, funciona mejor que una simple regresión logística en el sentido de que alcanza el mismo nivel de capacidad de predicción con menos ratios contables, lo que lleva a una mejor interpretación del modelo y, por lo tanto, a una mejor comprensión del proceso de quiebra empresarial