Optimización heurística de un nuevo tipo de cercha pretensada

Acaban de publicarnos un artículo en Materials, revista indexada en el primer cuartil del JCR. En este caso se ha optimizado, mediante un algoritmo de optimización heurística, un nuevo tipo de cercha metálica pretensada. El trabajo se enmarca dentro del proyecto de investigación HYDELIFE que dirijo como investigador principal en la Universitat Politècnica de València. En este caso, se trata de una colaboración entre nuestro grupo de investigación e investigadores de Georgia.

Este artículo presenta nuevos enfoques para el cálculo, el diseño y la optimización de cerchas pretensadas con un elemento de unión. Los sistemas estructurales con grandes luces, como cerchas, vigas, pórticos, etc., están sometidos a un riesgo considerable de pérdida de capacidad de carga debido a los diferentes tipos de cargas utilizadas. Algunos métodos de diseño tradicionales definen los valores del pretensado en el elemento de unión y las fuerzas internas en los elementos de la celosía para evitar esta pérdida de capacidad de carga. Sin embargo, la precisión y los límites de la determinación de las fuerzas no son necesariamente conocidos. Los autores proponen un nuevo tipo de celosía pretensada y algunos nuevos enfoques en el proceso de diseño y cálculo para resolver estos inconvenientes. Los principales objetivos del estudio fueron diseñar una innovadora y nueva forma geométrica de celosía arqueada pretensada, que permite el desarrollo de una fuerza de pretensado de alto valor, para optimizar una nueva celosía para reducir el peso propio, aumentando la capacidad de carga en comparación con sus análogos. Durante el estudio se empleó el recocido simulado. Un nuevo avance en la optimización de la celosía arqueada pretensada sugerido por los autores reduce el peso propio y mejora la capacidad de carga de la celosía entre un 8 y un 17%, dependiendo de la luz.

Abstract:

This paper represents new approaches for calculating, designing, and optimizing prestressed arched trusses with a tie member. Structural systems with long spans, such as trusses, beams, frames, etc., are subjected to a considerable/substantial risk of losing load-carrying capacity because of the different types of loads used. Some traditional design methods define the values of prestressing force in the tie member and internal forces in the truss elements to avoid this load capacity loss. However, the accuracy and limits of the determination of the forces are not necessarily known. The authors offer a new type of prestressed arched truss and some new approaches in the design and calculation process to solve these disadvantages. The study’s main objectives were to design an innovative and new geometric form of prestressed arched truss, which allows the development of high-value prestressing force, to optimize a new truss for reducing self-weight, increasing load-carrying capacity compared to its analogs. The force, stiffness matrix, and simulated annealing methods were used during the study. A new advance to the optimization of prestressed arched truss suggested by the authors reduces the self-weight and improves the load capacity of the truss by 8–17%, depending on the span.

Keywords:

Prestressed truss; stiffness matrix method; tensile element; compressed element; optimization; simulated annealing.

Reference:

PARTSKHALADZE, G.; ALCALÁ, J.; MEDZMARIASHVILI, E.; CHAVLESHVILI, G.; SURGULADZE, B., I.; YEPES, V. (2022). Heuristic Optimization of a New Type Prestressed Arched Truss. Materials, 15(22): 8144. DOI:10.3390/ma15228144

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Diseño óptimo de un puente mixto basado en un algoritmo de inteligencia de enjambre discreto

Acaban de publicarnos un artículo en la revista Structural and Multidisciplinary Optimization (revista indexada en el JCR en el primer cuartil) sobre la optimización de puentes mixtos de hormigón y acero usando un algoritmo de inteligencia de enjambre discreto y funciones de transferencia. El trabajo se enmarca dentro del proyecto de investigación HYDELIFE que dirijo como investigador principal en la Universitat Politècnica de València.

La optimización de puentes puede ser compleja debido al gran número de variables que intervienen en el problema. En este trabajo se ha ejecutado dos optimizaciones de puentes mixtos de sección en cajón, considerando el coste y las emisiones de CO₂ como funciones objetivo. Tomar las emisiones de CO₂ como función objetivo permite añadir criterios de sostenibilidad para comparar los resultados con el coste. Se han aplicado las metaheurísticas SAMO2, SCA y Jaya para alcanzar este objetivo. Se implementaron funciones de transferencia para adaptar SCA y Jaya a la naturaleza discontinua del problema de optimización del puente. Además, se ha llevado a cabo un Diseño de Experimentos para afinar el algoritmo y establecer sus parámetros. En consecuencia, se ha observado que SCA muestra valores similares para la función objetivo de coste que SAMO2, pero mejora el tiempo computacional en un 18% a la vez que obtiene valores más bajos para la desviación del resultado de la función objetivo. A partir de un análisis de optimización de costes y CO₂, se observa una reducción de 2,51 kg de CO₂ por cada euro reducido utilizando técnicas metaheurísticas. Además, para ambos objetivos de optimización, se comprueba que la adición de celdas a las secciones de los puentes mejora no solo el comportamiento de la sección, sino también los resultados de la optimización. Por último, los resultados muestran que el diseño propuesto de doble acción mixta en los apoyos permite eliminar los rigidizadores longitudinales continuos dispuestos en el ala inferior en este estudio.

Abstract:

Bridge optimization can be complex because of the large number of variables involved in the problem. In this paper, two box-girder steel–concrete composite bridge single objective optimizations have been carried out considering cost and CO₂ emissions as objective functions. Taking CO₂ emissions as an objective function allows adding sustainable criteria to compare the results with cost. SAMO2, SCA, and Jaya metaheuristics have been applied to reach this goal. Transfer functions have been implemented to fit SCA and Jaya to the discontinuous nature of the bridge optimization problem. Furthermore, a Design of Experiments has been conducted to tune the algorithm and set its parameters. Consequently, it has been observed that SCA shows similar values for objective cost function as SAMO2 but improves computational time by 18% while also getting lower values for the objective function result deviation. From a cost and CO₂ optimization analysis, it has been observed that a reduction of 2.51 kg CO₂ is obtained by each euro reduced using metaheuristic techniques. Moreover, for both optimization objectives, it is observed that adding cells to bridge cross-sections improves not only the section behavior but also the optimization results. Finally, it is observed that the proposed design of double composite action in the supports allows this study to remove continuous longitudinal stiffeners in the bottom flange.

Keywords:

Swarm intelligence; Steel–concrete composite structures; Bridges; Optimization; Metaheuristics; Sustainability

Reference:

MARTÍNEZ-MUÑOZ, D.; GARCÍA, J.; MARTÍ, J.V.; YEPES, V. (2022). Optimal design of steel-concrete composite bridge based on a transfer function discrete swarm intelligence algorithm. Structural and Multidisciplinary Optimization, 65:312. DOI:10.1007/s00158-022-03393-9

El artículo está publicado en abierto, por lo que podéis realizar su descarga gratuita en este enlace: https://link.springer.com/article/10.1007/s00158-022-03393-9

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Special Issue: “Machine Learning, Metaheuristics and Combinatorial Optimization Problems”

 

 

 

 

 

Mathematics (ISSN 2227-7390) is a peer-reviewed open-access journal that provides an advanced forum for studies related to mathematics and is published monthly online by MDPI.

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Impact Factor:  2.592 (2021) ; 5-Year Impact Factor: 2.542 (2021)  (First decile JCR journal) JCR – Q1 (Mathematics) / CiteScore – Q1 (General Mathematics)

Special Issue “Machine Learning, Metaheuristics and Combinatorial Optimization Problems”

Deadline for manuscript submissions: 10 February 2023.

Special Issue Editors

Prof. Dr. Víctor Yepes E-Mail Website SciProfiles Guest Editor
Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain
Interests: multiobjective optimization; structures optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty
Special Issues, Collections and Topics in MDPI journals
Dr. José Antonio García E-Mail Website Guest Editor
Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
Interests: optimization; deep learning; operations research; artificial intelligence applications to industrial problems
Special Issues, Collections and Topics in MDPI journals
Dr. Broderick Crawford E-Mail Website SciProfiles Guest Editor
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
Interests: information systems; management information systems; operations research; constraint satisfaction problems; collaboration of solvers

Special Issue Information

Dear Colleagues,

Complex combinatorial problems have been successfully addressed through metaheuristic techniques. However, as the size of the problem increases, so does the need for robust optimization algorithms. An interesting method of strengthening these algorithms is through the application of hybrid techniques, specifically the hybridization of machine learning and metaheuristics. We invite researchers to submit articles on combined optimization and hybrid techniques for this Special Issue. Benchmarking problems or applications in the industry are also of interest.

The areas of machine learning and data science have received considerable research interest in recent years. These techniques have strongly excelled in supporting decision-making in complex and data-intensive scenarios. In this Special Issue, we are additionally interested in contributions to machine learning applications in the industry.

Prof. Víctor Yepes
Dr. José Antonio García
Dr. Broderick Crawford
Guest Editors

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Submitted manuscripts should not have been published previously nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Aplicación de optimización kriging para la búsqueda de estructuras óptimas robustas

Redheugh Bridge, Newcastle. © Copyright Stephen Richards and licensed for reuse under this Creative Commons Licence.

En todos los problemas estructurales existe una variabilidad o incertidumbre asociada. En el diseño de estructuras hay parámetros de diseño como las dimensiones de la estructura, las características mecánicas de los materiales o las cargas de diseño que pueden tener variaciones respecto al valor de diseño. Lo mismo ocurre a la hora de valorar una función objetivo asociada la estructura. Por un lado, a la hora de diseñar una estructura, el valor nominal utilizado es aquel que tiene una baja probabilidad de ocurrir (por ejemplo, la resistencia característica del hormigón es aquella que tiene una probabilidad del 5% de fallo). Además, se asignan coeficientes de seguridad asociados a una probabilidad de fallo determinada. Por otro lado, a la hora de valorar una función objetivo, como el coste o algún impacto medioambiental, el valor unitario de esta función suele ser la media. Dado este enfoque, la optimización estructural se convierte en una optimización determinista que desprecia los efectos de la incertidumbre asociada. Esto significa que la estructura tiene un comportamiento óptimo solo bajo las condiciones definidas inicialmente, pudiendo la respuesta variar significativamente cuando los valores se alejan de los valores de diseño.

A continuación os dejo una comunicación que presentamos en el 5th International Conference on Mechanical Models in Structural Engineering, que se celebró del 23 al 25 de octubre de 2019 en Alicante (España). Se trata de la optimización de un puente de sección en cajón de hormigón postesado utilizando un metamodelo tipo Krigring.

Abstract:

All the structural problems have an associated variability or uncertainty. In the design of structures, there are parameters such as the dimensions of the structure, the mechanical characteristics of the materials, or the loads that can have variations concerning the design value. The goal of robust design optimization is to obtain the optimum design and be less sensitive to variations of these uncertain initial parameters. The main limitation of the robust design optimization is the high computational cost required due to the high number of optimizations that must be made to assess the sensitivity of the objective response of the problem. For this reason, the kriging model is applied to carry out the optimization process more efficiently. This work will apply robust design optimization to a continuous pedestrian bridge of prestressed concrete and box sections.

Keywords:

Post-tensioned concrete; Box-girder bridge; Robust design optimization; RDO; Kriging

Reference:

YEPES, V.; PENADÉS-PLÀ, V.; GARCÍA-SEGURA, T. (2019). Aplicación de optimización Kriging para la búsqueda de estructuras óptimas robustas. 5th International Conference on Mechanical Models in Structural Engineering, CMMoST 2019, 23-25 oct 2019, Alicante, Spain, pp. 81-94. ISBN: 978–84–17924–58–4

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Optimización de las emisiones de CO₂ en la construcción de puentes losa postesados utilizando metamodelos

Acaban de publicarnos un artículo en la revista Materials, revista indexada en el primer cuartil del JCR. En este caso se ha optimizado, mediante un metamodelo tipo Kriging, las emisiones de CO₂ de un puente losa postesado aligerado. El trabajo se enmarca dentro del proyecto de investigación HYDELIFE que dirijo como investigador principal en la Universitat Politècnica de València.

Se trata de un trabajo de investigación en el que se ha propuesto una metodología novedosa, bifase, que utilizando un metamodelo tipo Kriging y con un muestreo inteligente del espacio de soluciones, permite optimizar problemas de alto nivel de complejidad computacional. Es el caso de las estructuras de hormigón, y en este trabajo en particular, de un tablero de puente losa pretensado aligerado. Por tanto, el objetivo general de este trabajo es proponer y comprobar la aplicabilidad de una metodología que permita la reducción energética y reducción de las emisiones de CO₂ en la construcción del tablero de un puente losa pretensado aligerado. La metodología propuesta tiene carácter general, pudiéndose aplicar a la optimización de cualquier otro tipo de estructura para optimizar distintas funciones objetivo. El diseño de la metodología propuesta presenta dos fases secuenciales de optimización, la primera fase de diversificación y la segunda fase de intensificación de la búsqueda de los óptimos.

Abstract:

This paper deals with optimizing embedded carbon dioxide (CO₂) emissions using surrogate modeling, whether it is the deck of a post-tensioned cast-in-place concrete slab bridge or any other design structure. The main contribution of this proposal is that it allows optimizing structures methodically and sequentially. The approach presents two sequential phases of optimization, the first one of diversification and the second one of intensification of the search for optimums. Finally, with the amount of CO₂ emissions and the differentiating characteristics of each design, a heuristic optimization based on a Kriging metamodel is performed. An optimized solution with lower emissions than the analyzed sample is obtained. If CO₂ emissions were to be reduced, design recommendations would be to use slendernesses as high as possible, in the range of 1/30, which implies a more significant amount of passive reinforcement. This increase in passive reinforcement is compensated by reducing the measurement of concrete and active reinforcement. Another important conclusion is that reducing emissions is related to cost savings. Furthermore, it has been corroborated that for a cost increase of less than 1%, decreases in emissions emitted into the atmosphere of more than 2% can be achieved.

Keywords:

CO₂ emission; optimization; metamodel; Kriging; post-tensioned concrete; structural optimization

Reference:

YEPES-BELLVER, L.; BRUN-IZQUIERDO, A.; ALCALÁ, J.; YEPES, V. (2022). CO₂-optimization of post-tensioned concrete slab-bridge decks using surrogate modeling. Materials, 15(14):4776. DOI:10.3390/ma15144776

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Optimización de la vía en placa mediante metamodelos para mejorar la sostenibilidad de la construcción ferroviaria

Acaban de publicarnos un artículo en el Journal of Construction Engineering and Management-ASCE, revista indexada en el primer cuartil del JCR. Se trata de optimizar la vía en placa mediante metamodelos para mejorar la sostenibilidad de la construcción ferroviaria. El trabajo se enmarca dentro del proyecto de investigación HYDELIFE que dirijo como investigador principal en la Universitat Politècnica de València.

Este artículo no está publicado en abierto, pero podéis encontrarlo, solicitándolo, en esta dirección: https://www.researchgate.net/publication/360243758_Slab_Track_Optimization_Using_Metamodels_to_Improve_Rail_Construction_Sustainability o bien descargarlo directamente de la página web de ASCE: https://ascelibrary.org/doi/full/10.1061/%28ASCE%29CO.1943-7862.0002288

El ferrocarril es un medio de transporte eficaz, sin embargo, la construcción y el mantenimiento de las vías férreas tienen un impacto medioambiental importante en términos de emisiones de CO2 y uso de materias primas. Esto es especialmente cierto en el caso de la vía en placa, pues necesitan grandes cantidades de hormigón. También son más caras de construir que las vías convencionales con balasto, pero requieren menos mantenimiento y presentan otras ventajas que las convierten en una buena alternativa, especialmente para las líneas de alta velocidad. Para contribuir a un ferrocarril más sostenible, este trabajo pretende optimizar el diseño de una de las tipologías de vía en placa más comunes: RHEDA 2000. El objetivo principal es reducir la cantidad de hormigón necesaria para construir la losa sin comprometer su rendimiento y durabilidad. Para ello, se utilizó un modelo basado en el método de los elementos finitos (MEF) de la vía, emparejado con un metamodelo de kriging que permite analizar múltiples opciones de espesor de la losa y resistencia del hormigón de forma puntual. Mediante kriging, se obtuvieron soluciones óptimas que se validaron a través del modelo MEF para garantizar el cumplimiento de las restricciones mecánicas y geométricas predefinidas. Partiendo de una configuración inicial con una losa de 30 cm de hormigón con una resistencia característica de 40 MPa, se llegó a una solución optimizada, consistente en una losa de 24 cm de hormigón con una resistencia de 45 MPa, que arroja una reducción de costes del 17,5%. Este proceso puede aplicarse ahora a otras tipologías de losas para obtener diseños más sostenibles.

Abstract:

Railways are an efficient transport mode, but building and maintaining railway tracks have a significant environmental impact in terms of CO2 emissions and the use of raw materials. This is particularly true for slab tracks, which require large quantities of concrete. They are also more expensive to build than conventional ballasted tracks, but require less maintenance and have other advantages that make them a good alternative, especially for high-speed lines. To contribute to more sustainable railways, this paper aims to optimize the design of one of the most common slab track typologies: RHEDA 2000. The main objective is to reduce the amount of concrete required to build the slab without compromising its performance and durability. To do so, a model based on the finite-element method (FEM) of the track was used, paired with a kriging metamodel to allow analyzing multiple options of slab thickness and concrete strength in a timely manner. By means of kriging, optimal solutions were obtained and then validated through the FEM model to ensure that predefined mechanical and geometrical constraints were met. Starting from an initial setup with a 30-cm slab made of concrete with a characteristic strength of 40 MPa, an optimized solution was reached, consisting of a 24-cm slab made of concrete with a strength of 45 MPa, which yields a cost reduction of 17.5%. This process may be now applied to other slab typologies to obtain more sustainable designs.

Keywords:

Slab track; Optimization; Latin hypercube; Kriging; Finite-element method (FEM).

Reference:

MARTÍNEZ FERNÁNDEZ, P.; VILLALBA SANCHIS, I.; INSA FRANCO, R.; YEPES, V. (2022). Slab track optimisation using metamodels to improve rail construction sustainabilityJournal of Construction Engineering and Management, 148(7):04022053. DOI:10.1061/(ASCE)CO.1943-7862.0002288

Optimización de tableros de puentes mixtos con metaheurística de trayectoria

Variables de la sección transversal del puente mixto

La optimización de puentes es un problema complejo debido al gran número de variables que intervienen. En este trabajo se ha realizado la optimización de un puente mixto en cajón considerando el coste como función objetivo. Para ello se ha aplicado el Recocido Simulado (SA) como ejemplo de algoritmo basado en la búsqueda de soluciones mediante trayectorias para la optimización de la estructura. Se observa que la adición de celdas a las secciones transversales del puente mejora no sólo el comportamiento de la sección sino también los resultados de la optimización. Finalmente, se observa que el diseño propuesto de doble acción compuesta materializando losas en el ala inferior sobre apoyos, permite eliminar los rigidizadores longitudinales continuos. Este método automatiza el proceso de optimización de un diseño inicial de un puente de material compuesto, que tradicionalmente se ha basado en la propia experiencia del técnico, permitiendo alcanzar resultados de forma más eficiente.

Referencia:

MARTÍNEZ-MUÑOZ, D.; SÁNCHEZ-GARRIDO, A.J.; MARTÍ, J.V.; YEPES, V. (2021). Composite bridge deck optimization with trajectory-based algorithms. 6th International Conference on Mechanical Models in Structural Engineering, CMMoST 2021, 1-3 December, Valladolid, Spain, pp. 174-187. ISNB: 978-84-09-39323-7

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Hacia un mapa de conocimiento algorítmico de optimización de la industria AEC-AI (Arquitectura, Ingeniería, Construcción e Inteligencia Artificial)

Acaban de publicarnos un artículo en la revista IEEE Access, revista de alto impacto indexada en el JCR. En este caso se ha realizado un análisis conceptual macroscópico de la industria AEC-AI (Arquitectura, Ingeniería, Construcción e Inteligencia Artificial). El trabajo se enmarca dentro del proyecto de investigación HYDELIFE que dirijo como investigador principal en la Universitat Politècnica de València.

La industria de la arquitectura, la ingeniería y la construcción (AEC) constituye uno de los sectores productivos más relevantes, por lo que también produce un alto impacto en los equilibrios económicos, la estabilidad de la sociedad y los desafíos globales en el cambio climático. En cuanto a su adopción de tecnologías, aplicaciones y procesos también se reconoce por su status-quo, su lento ritmo de innovación, y los enfoques conservadores. Sin embargo, una nueva era tecnológica -la Industria 4.0 alimentada por la IA- está impulsando los sectores productivos en un panorama sociopolítico y de competencia tecnológica global altamente presionado. En este trabajo, desarrollamos un enfoque adaptativo para la minería de contenido textual en el corpus de investigación de la literatura relacionada con las industrias de la AEC y la IA (AEC-AI), en particular en su relación con los procesos y aplicaciones tecnológicas. Presentamos un enfoque de primera etapa para una evaluación adaptativa de los algoritmos de IA, para formar una plataforma integradora de IA en la industria AEC, la industria AEC-AI 4.0. En esta etapa, se despliega un método adaptativo macroscópico para caracterizar la “Optimización”, un término clave en la industria AEC-AI, utilizando una metodología mixta que incorpora el aprendizaje automático y el proceso de evaluación clásico. Nuestros resultados muestran que el uso eficaz de los metadatos, las consultas de búsqueda restringidas y el conocimiento del dominio permiten obtener una evaluación macroscópica del concepto objetivo. Esto permite la extracción de un mapeo de alto nivel y la caracterización de la estructura conceptual del corpus bibliográfico. Los resultados son comparables, a este nivel, a las metodologías clásicas de revisión de la literatura. Además, nuestro método está diseñado para una evaluación adaptativa que permita incorporar otras etapas.

Abstract:

The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era – Industry 4.0 fueled by AI- is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a first stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize “Optimization,” a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.

Keywords:

Architecture, engineering and construction, AEC, artificial intelligence, literature corpus, machine learning, optimization algorithms, knowledge mapping and structure

Reference:

MAUREIRA, C.; PINTO, H.; YEPES, V.; GARCÍA, J. (2021). Towards an AEC-AI industry optimization algorithmic knowledge mapping. IEEE Access, 9:110842-110879. DOI:10.1109/ACCESS.2021.3102215

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Open Access Book: Optimization for Decision Making II

Tengo el placer de compartir con todos vosotros, totalmente en abierto, un libro que he editado junto con el profesor de la Universidad de Zaragoza, José María Moreno Jiménez. La labor de editar libros científicos es una oportunidad de poder seleccionar aquellos autores y temas que destacan en un ámbito determinado. En este caso, la optimización en la toma de decisiones.

Este libro forma parte de una serie sobre toma de decisiones. Podéis descargar también el primer libro de la serie en la siguiente dirección: https://victoryepes.blogs.upv.es/2020/10/09/open-access-book-optimization-for-decision-making/

Además, resulta gratificante ver que el libro se encuentra editado en abierto, por lo que cualquiera de vosotros os lo podéis descargar sin ningún tipo de problema en esta entrada del blog. También os lo podéis descargar, o incluso pedirlo en papel, en la página web de la editorial MPDI: https://www.mdpi.com/books/pdfview/book/3129

 

Referencia:

YEPES, V.; MORENO-JIMÉNEZ, J.M. (Eds.) (2020). Optimization for Decision Making II. MPDI, 302 pp., Basel, Switzerland. ISBN 978-3-03943-607-1

 

Preface to ”Optimization for Decision Making II”

Decision making is one of the distinctive activities of the human being; it is an indication of the degree of evolution, cognition and freedom of the species. In the past, until the end of the 20th century, scientific decision making was based on the paradigms of substantive rationality (normative approach) and procedural rationality (descriptive approach). Since the beginning of the 21st century and the advent of the Knowledge Society, decision making has been enriched with new constructivist, evolutionary and cognitive paradigms that aim to respond to new challenges and needs; especially the integration into formal models of the intangible, subjective and emotional aspects associated with the human factor, and the participation in decision-making processes of spatially distributed multiple actors that intervene in a synchronous or an asynchronous manner. To help address and resolve these types of questions, this book comprises 16 chapters that present a series of decision models, methods and techniques and their practical applications in the fields of economics, engineering and social sciences. The chapters collect the papers included in the “Optimization for Decision Making II” Special Issue of the Mathematics journal, 2020, 8(6), first decile of the JCR 2019 in the Mathematics category. We would like to thank both the MDPI publishing and editorial staff for their excellent work, as well as the 51 authors who have collaborated in its preparation. The papers cover a wide spectrum of issues related to the scientific resolution of problems; in particular, related to decision making, optimization, metaheuristics, and multi-criteria decision making. We hope that the papers, with their undoubted mathematical content, can be of use to academics and professionals from the many branches of knowledge (philosophy, psychology, economics, mathematics, decision science, computer science, artificial intelligence, neuroscience and more) that have, from such diverse perspectives, approached the study of decision making, an essential aspect of human life and development.

Víctor Yepes, José M. Moreno-Jiménez
Editors

About the Editors

Víctor Yepes Full Professor of Construction Engineering; he holds a Ph.D. degree in civil engineering. He serves at the Department of Construction Engineering, Universitat Politecnica de Valencia, Valencia, Spain. He has been the Academic Director of the M.S. studies in concrete materials and structures since 2007 and a Member of the Concrete Science and Technology Institute (ICITECH). He is currently involved in several projects related to the optimization and life-cycle assessment
of concrete structures as well as optimization models for infrastructure asset management. He is currently teaching courses in construction methods, innovation, and quality management. He authored more than 250 journals and conference papers including more than 100 published in the journal quoted in JCR. He acted as an Expert for project proposals evaluation for the Spanish Ministry of Technology and Science, and he is the Main Researcher in many projects. He currently serves as the Editor-in-Chief of the International Journal of Construction Engineering and Management and a member of the editorial board of 12 international journals (Structure & Infrastructure Engineering, Structural Engineering and Mechanics, Mathematics, Sustainability, Revista de la Construcción, Advances in Civil Engineering, and Advances in Concrete Construction, among others).

José María Moreno-Jiménez Full Professor of Operations Research and Multicriteria Decision Making, received the degrees in mathematics and economics as well as a Ph.D. degree in applied mathematics from the University of Zaragoza, Spain; where he is teaching from the course 1980–1981. He is the Head of the Quantitative Methods Area in the Faculty of Economics and Business of the University of Zaragoza from 1997, the Chair of the Zaragoza Multicriteria Decision Making Group from 1996, a member of the Advisory Board of the Euro Working Group on Decision Support Systems from 2017, and an Honorary Member of the International Society on Applied Economics ASEPELT from 2019. He has also been the President of this international scientific society (2014–2018) and the Coordinator of the Spanish Multicriteria Decision Making Group (2012–2015). His research interests are in the general area of Operations Research theory and practice, with an emphasis on multicriteria decision making, electronic democracy/cognocracy, performance analysis, and industrial and technological diversification. He has published more than 250 papers in scientific books and journals in the most prestigious editorials and is a member of the Editorial Board of several national and international journals.

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Special Issue “Optimization for Decision Making III”

 

 

 

 

 

Mathematics (ISSN 2227-7390) is a peer-reviewed open access journal which provides an advanced forum for studies related to mathematics, and is published monthly online by MDPI.

  • Open Access – free for readers, with article processing charges (APC) paid by authors or their institutions.
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  • Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 21.7 days after submission; acceptance to publication is undertaken in 5.3 days (median values for papers published in this journal in the second half of 2018).
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Impact Factor: 1.747 (2019)  (First decile JCR journal)

Special Issue “Optimization for Decision Making III”

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Guest Editor 

Prof. Víctor Yepes
Universitat Politècnica de València, Spain
Website | E-Mail
Interests: multiobjective optimization; structures optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty

Guest Editor 

Prof. José M. Moreno-Jiménez
Universidad de Zaragoza
Website | E-Mail
Interests: multicriteria decision making; environmental selection; strategic planning; knowledge management; evaluation of systems; logistics and public decision making (e-government, e-participation, e-democracy and e-cognocracy)

Special Issue Information

Dear Colleagues,

In the current context of the electronic governance of society, both administrations and citizens are demanding greater participation of all the actors involved in the decision-making process relative to the governance of society. In addition, the design, planning, and operations management rely on mathematical models, the complexity of which depends on the detail of models and complexity/characteristics of the problem they represent. Unfortunately, decision-making by humans is often suboptimal in ways that can be reliably predicted. Furthermore, the process industry seeks not only to minimize cost, but also to minimize adverse environmental and social impacts. On the other hand, in order to give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and application of optimization techniques to support decisions is particularly complex, and a wide range of optimization techniques and methodologies are used to minimize risks or improve quality in making concomitant decisions. In addition, a sensitivity analysis should be done to validate/analyze the influence of uncertainty regarding decision-making.

Prof. Víctor Yepes
Prof. José Moreno-Jiménez
Guest Editors

Manuscript Submission Information

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Keywords

  • Multicriteria decision making
  • Optimization techniques
  • Multiobjective optimization