Open Access Book: Optimization for Decision Making

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.

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/2958

Referencia:

YEPES, V.; MORENO-JIMÉNEZ, J.M. (Eds.) (2020). Optimization for Decision Making. MPDI, 277 pp., Basel, Switzerland. ISBN: 978-3-03943-221-9

Preface to ”Optimization for Decision Making”

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 asynchronous manner. To help address and resolve these types of questions, this book comprises 13 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” Special Issue of the Mathematics journal, 2019, 7(3), first decile of the JCR 2019 in the Mathematics category. We would like to thank both the MDPI publishing editorial team, for their excellent work, and the 47 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, simulation, 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 María 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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Sesión temática en CMN2021: Optimization, metaheuristics and evolutionary algorithms in civil engineering

En el marco del próximo congreso CMN2021 (Congress on Numerical Methods in Engineering) que se celebrará en Las Palmas de Gran Canaria del 28 al 30 de junio de 2021, hemos organizado una sesión temática coordinada por David Greiner, Diogo Ribeiro y Víctor Yepes que versa sobre optimización, metaheurísticas y algoritmos evolutivos en ingeniería civil. Os dejo a continuación una breve descripción del congreso y un resumen de la sesión temática propuesta.

El objetivo del Congreso de Métodos Numéricos en Ingeniería (CMN) es actuar como un foro en que se recopilen los trabajos científicos y técnicos más relevantes en el área de los métodos numéricos y la mecánica computacional, así como sus aplicaciones prácticas. CMN 2021, organizado conjuntamente por las sociedades de métodos numéricos española (SEMNI), portuguesa (APMTAC) y por el Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI) de la Universidad de Las Palmas de Gran Canaria (ULPGC). Los anteriores congresos conjuntos de ambas sociedades fueron celebrados en Madrid (2002), en Lisboa (2004), en Granada (2005), Porto (2007), Barcelona (2009), Coimbra (2011), Bilbao (2013), Lisboa (2015), Valencia (2017) y Minho (2019). Habiendo sido Las Palmas de Gran Canaria la sede del Primer Congreso CMN organizado por SEMNI en 1990, (General Chairs: Gabriel Winter y Miguel Galante), retorna 31 años después a su primera sede. El programa científico del CMN 2021 estará estructurado en sesiones temáticas según las distintas especialidades de los métodos numéricos. Las comunicaciones presentadas en el congreso constituirán una referencia de los avances recientes y de las líneas de trabajo futuras. Asimismo, investigadores internacionales de reconocido prestigio impartirán una serie de conferencias plenarias. El enlace a la web del congreso es la siguiente: https://congress.cimne.com/cmn2021

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Optimización del mantenimiento del pavimento en carreteras mediante GRASP

La insuficiente inversión en el sector público junto con programas ineficaces de infraestructura de mantenimiento conducen a altos costos económicos a largo plazo. Por lo tanto, los responsables de la infraestructura necesitan herramientas prácticas para maximizar la eficacia a largo plazo de los programas de mantenimiento. En el artículo que os presento se describe una herramienta de optimización basada en un procedimiento híbrido de búsqueda aleatoria y adaptativa (GRASP) considerando la aceptación del umbral (TA) con restricciones relajadas. Esta herramienta facilita el diseño de programas de mantenimiento óptimos sujetos a restricciones presupuestarias y técnicas, explorando el efecto de diferentes escenarios presupuestarios en el estado general de la red. La herramienta de optimización se aplica a un estudio de caso, demostrando su eficiencia para analizar datos reales. Se demuestra que los programas de mantenimiento optimizado rinden un 40% más a largo plazo que los programas tradicionales basados en una estrategia reactiva. Para ampliar los resultados obtenidos en este estudio de caso, también se optimizaron un conjunto de escenarios simulados, basados en el rango de valores encontrados en el ejemplo real. El trabajo concluye que este algoritmo de optimización mejora la asignación de los fondos de mantenimiento con respecto a la obtenida con una estrategia reactiva tradicional. El análisis de sensibilidad de una gama de escenarios presupuestarios indica que el nivel de financiación en los primeros años es un factor impulsor a largo plazo de los programas de mantenimiento óptimo.

Referencia:

YEPES, V.; TORRES-MACHÍ, C.; CHAMORRO, A.; PELLICER, E. (2016). Optimal pavement maintenance programs based on a hybrid greedy randomized adaptive search procedure algorithm. Journal of Civil Engineering and Management, 22(4):540-550. DOI:10.3846/13923730.2015.1120770

Os dejo a continuación la versión autor del artículo.

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Algoritmo híbrido de búsqueda del cuco para optimizar muros de contrafuertes

Acaban de publicarnos un artículo en la revista Mathematics, revista indexada en el primer cuartil del JCR. En este artículo se presenta un algoritmo híbrido de búsqueda del cuco y de clasificación no supervisada para optimizar el coste y las emisiones de CO2 de un muro de contrafuertes. El trabajo se enmarca dentro del proyecto de investigación DIMALIFE que dirijo como investigador principal en la Universitat Politècnica de València.

La Búsqueda Cuco se basa en la estrategia de reproducción de algunas especies de pájaros cucos. Éstos pájaros dejan sus huevos en los nidos de otros pájaros de otras especies para que éstas los críen, expulsando incluso los huevos del nido invadido. Si el pájaro anfitrión se percata que el huevo no es el propio, lo sacará del nido o directamente lo abandonará y construirá otro nido.

Por su parte, K-means es un algoritmo de clasificación no supervisada (clusterización) que agrupa objetos en k grupos basándose en sus características. El agrupamiento se realiza minimizando la suma de distancias entre cada objeto y el centroide de su grupo o cluster.

En este artículo se propone un algoritmo híbrido, en el que la metaheurística de búsqueda del cuco se utiliza como mecanismo de optimización en espacios continuos y la técnica de aprendizaje no supervisada k-means para discretizar las soluciones. Se diseña un operador aleatorio para determinar la contribución del operador k-means en el proceso de optimización. Se comparan los mejores valores, los promedios y los rangos intercuartiles de las distribuciones obtenidas. Los resultados muestran que el operador k-means contribuye significativamente a la calidad de las soluciones y que nuestro algoritmo es altamente competitivo.

Abstract

The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO₂ emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

Keywords

CO2emission; earth-retaining walls; optimization; k-means; cuckoo search

Referencia:

GARCÍA, J.; YEPES, V.; MARTÍ, J.V. (2020). A hybrid k-means cuckoo search algorithm applied to the counterfort retaining walls problem. Mathematics, 8(4), 555. DOI:10.3390/math8040555

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Optimización de emisiones de CO2 y costes de muros de contrafuertes con el algoritmo del agujero negro

Acaban de publicarnos un artículo en la revista Sustainability, revista indexada en JCR. En este artículo minimizamos las emisiones de CO2 en la construcción de un muro de contrafuertes de hormigón armado usando la metaheurística del agujero negro (Black Hole Algorithm). El trabajo se enmarca dentro del proyecto de investigación DIMALIFE que dirijo como investigador principal en la Universitat Politècnica de València.

La optimización del costo y de las emisiones de CO2 en los muros de contención de tierras es relevante, pues estas estructuras se utilizan muy frecuentemente en la ingeniería civil. La optimización de los costos es esencial para la competitividad de la empresa constructora, y la optimización de las emisiones es relevante en el impacto ambiental de la construcción. Para abordar la optimización se utilizó la metaheurística de los agujeros negros, junto con un mecanismo de discretización basado en la normalización mínimo-máxima. Se evaluó la estabilidad del algoritmo con respecto a las soluciones obtenidas; se analizaron los valores de acero y hormigón obtenidos en ambas optimizaciones. Además, se compararon las variables geométricas de la estructura. Los resultados muestran un buen rendimiento en la optimización con el algoritmo de agujero negro.

Abstract

The optimization of the cost and CO $_{2}$ emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min–max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO $_{2}$ emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.

Keywords

CO2 emission; earth-retaining walls; optimization; black hole; min–max discretization

Reference:

YEPES, V.; MARTÍ, J.V.; GARCÍA, J. (2020). Black hole algorithm for sustainable design of counterfort retaining walls. Sustainability, 12, 2767. DOI:10.3390/su12072767

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Optimización de pasarelas de sección en cajón mediante metamodelos Kriging

Uno de los objetivos del proyecto DIMALIFE es la obtención de procedimientos novedosos y rápidos para optimizar estructuras mediante metamodelos. Los algoritmos heurísticos siguen un proceso inteligente en el que se modifican las variables de diseño con el fin de optimizar la función objetivo y verificar las restricciones. Metodologías como la optimización del diseño basada en metamodelos, como es el caso del método Kriging, proporcionan una superficie de respuesta de la muestra que puede ser optimizada.

A continuación os dejo una comunicación que presentamos en el pasado congreso IALCCE 2018 en Gante (Bélgica) sobre la optimización de una pasarela hiperestática de sección en cajón de hormigón postesado. En este trabajo, la optimización heurística convencional y la optimización heurística basada en kriging se aplican al mismo estudio de caso. En este caso se trata de una pasarela peatonal continua de vigas de cajón. La comparación muestra las ventajas y desventajas de ambas metodologías. Espero que os sea de interés.

ABSTRACT:

The structural optimization aims to determine the best solutions for the project objectives while guaranteeing the structural constraints. The heuristic algorithms follow an intelligent process in which the design variables are modified for the purpose of optimizing the objective function and verify the constraints. Methodologies like metamodel-based design optimization or surrogate-based optimization carry out a pseudo optimization applicable to structures. The kriging method provides a response surface from the sample that can be optimized. In this paper, conventional heuristic optimization and kriging-based heuristic optimization are applied to the same case study. This case involves a continuous box-girder pedestrian bridge. The comparison of the methodologies shows the advantages and disadvantages of both methodologies. Furthermore, a major compression of both processes gain a better understanding of the methods and the most suitable cases.

REFERENCE:

PENADÉS-PLÀ, V.; GARCÍA-SEGURA, T.; YEPES, V.; MARTÍ, J.V. (2018). Kriging-based heuristic optimization of a continuous concrete box-girger pedestrian bridge. Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE 2018), Ganth (Belgium), October 2018, pp. 2753-2759. ISBN: 9781138626331

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Special Issue “Optimization for Decision Making II”

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.
High visibility: Indexed in the Science Citation Indexed Expanded – SCIE (Web of Science) from Vol. 4 (2016), Scopus, and Zentralblatt MATH from Vol. 3 (2015).
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).
Recognition of reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 1.105 (2018) (First quartile, JCR)

Special Issue “Optimization for Decision Making II”

Deadline for manuscript submissions: 29 February 2020.

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.

Keywords

Multicriteria decision making
Optimization techniques
Multiobjective optimization

Optimización de pórticos de hormigón armado con sistemas de agrupación de columnas

Nos acaban de publicar en la revista Automation in Construction (primer cuartil del JCR) un artículo relacionado con la optimización de pórticos de hormigón armado con sistemas de agrupación de columnas. Se trata de una colaboración con el profesor Moacir Kripka y está dentro del proyecto de investigación DIMALIFE.

Puedes descargar gratuitamente el artículo hasta el 22 de junio de 2019 en el siguiente enlace: https://authors.elsevier.com/a/1Y~vl3IhXMfb77

ABSTRACT

In structural design, it is common practice to adopt the same cross-sectiondimensions for a group of elements. This procedure is mainly for practical and aesthetic reasons, as well as to reduce labour costs, but it also has a positive effect of reducing the number of variables, which simplifies the usual trial and error design process. On the other hand, the total materials cost obtained is closely related to this grouping. Based on this, the present work aims to minimize the cost of reinforced concrete plane frames considering the automated grouping of columns. To achieve this objective, an optimization software was developed by the association of matrix structural analysis, dimensioning and optimization. The sections dimensions, the area of steel and the concrete strength of beams and columns were taken as design variables. For a given maximum number of groups, the optimum grouping and the corresponding values to design variables are obtained. The strategy proposed in this paper to obtain the grouping reduces significantly the number of infeasible candidate solutions during the search process and avoid the proposition of unrealistic designs. For the optimization, a variant of the Harmony Search method was adopted. Some structures were analyzed in order to validate the application of the proposed formulation, as well as to verify the influence of the grouping of elements on the final results. In these structures, it was possible to observe a significant additional reduction in the total cost when automated grouping is performed regarding a uniform grouping, even when a small number of groups is considered. For the 20-floor building frame analyzed, the cost reduction from uniform to automated grouping varied from 5.53 to 7.35%. The influence of the concrete strength on optimal results was also investigated, indicating a cost reduction of 9.74% from best (40 MPa) to worst case (20 MPa). In general, it can be concluded that, when applied in conjunction with the usual design variables, the proposed procedure can enable a significant additional economy, without affecting the structural safety.

KEYWORDS

Optimization; automated grouping; reinforced concrete; plane frames; harmony search

Reference:

BOSCARDIN, J. T.; YEPES, V.; KRIPKA, M. (2019). Optimization of reinforced concrete building frames with automated grouping of columns. Automation in Construction, 104: 331-340. DOI:10.1016/j.autcon.2019.04.024

Revisión de los métodos de optimización aplicados al consumo de energía en ferrocarriles

Acaban de publicarnos un artículo en la revista Journal of Cleaner Production, revista de ELSEVIER indexada en el primer decil del JCR. Se trata de un artículo de revisión del estado del arte donde se analizan 52 artículos científicos relacionados con el consumo energético en ferrocarriles. Se analizan dos áreas principales: las técnicas de modelización utilizadas para simular el movimiento de los trenes y el consumo de energía, y los métodos de optimización utilizados para conseguir una circulación ferroviaria más eficiente. Se describen brevemente los métodos más utilizados en cada caso y se analizan las principales tendencias encontradas. Además, se ha realizado un estudio estadístico para reconocer las relaciones entre los métodos y las variables de optimización. Se encontró que los modelos determinísticos basados en la ecuación de Davis son, con diferencia (85% de los trabajos revisados), los más comunes en términos de modelización. En cuanto a la optimización, los métodos meta-heurísticos son la opción preferida (57,8%), en particular los Algoritmos Genéticos. Este artículo forma parte de nuestra línea de investigación BRIDLIFE en la que se pretenden optimizar las infraestructuras atendiendo no sólo a su coste, sino al impacto ambiental y social que generan a lo largo de su ciclo de vida.

El artículo lo podéis descargar GRATUITAMENTE hasta el 3 de mayo de 2019 en el siguiente enlace: https://authors.elsevier.com/a/1YjHX3QCo9Uqa3

Abstract:

Railways are a rather efficient transport mean, and yet there is increasing interest in reducing their energy consumption and making them more sustainable in the current context of climate change. Many studies try to model, analyse and optimise the energy consumed by railways, and there is a wide diversity of methods, techniques and approaches regarding how to formulate and solve this problem. This paper aims to provide insight into this topic by reviewing up to 52 papers related to railways energy consumption. Two main areas are analysed: modelling techniques used to simulate train(s) movement and energy consumption, and optimisation methods used to achieve more efficient train circulations in railway networks. The most used methods in each case are briefly described and the main trends found are analysed. Furthermore, a statistical study has been carried out to recognise relationships between methods and optimisation variables. It was found that deterministic models based on the Davis equation are by far (85% of the papers reviewed) the most common in terms of modelling. As for optimisation, meta-heuristic methods are the preferred choice (57.8%), particularly Genetic Algorithms.

Keywords:

Railways

Energy efficiency

Modelling

Optimisation

Meta-heuristics

Reference:

MARTÍNEZ-FERNÁNDEZ, P.; VILLALBA-SANCHÍS, I.; INSA-FRANCO, R.; YEPES, V. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production, 222:153-162. DOI:10.1016/j.jclepro.2019.03.037

Special Issue “Optimization for Decision Making”

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.
High visibility: Indexed in the Science Citation Indexed Expanded – SCIE (Web of Science) from Vol. 4 (2016), Scopus from Vol. 5 (2017) and Zentralblatt MATH from Vol. 3 (2015).

Special Issue “Optimization for Decision Making”

Deadline for manuscript submissions: 31 May 2019

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 are 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 M. Moreno-Jiménez
Guest Editors

Manuscript Submission Information

Keywords

Multicriteria decision making
Optimization techniques
Multiobjective optimization