Problemas de corte: métodos exactos y aproximados para formulaciones mono y multi-objetivo

Los problemas de corte y empaquetado son una familia de problemas de optimización combinatoria que han sido ampliamente estudiados en numerosas áreas de la industria y la investigación, debido a su relevancia en una enorme variedad de aplicaciones reales. Son problemas que surgen en muchas industrias de producción donde se debe realizar la subdivisión de un material o espacio disponible en partes más pequeñas. Existe una gran variedad de métodos para resolver este tipo de problemas de optimización. A la hora de proponer un método de resolución para un problema de optimización, es recomendable tener en cuenta el enfoque y las necesidades que se tienen en relación al problema y su solución. Las aproximaciones exactas encuentran la solución óptima, pero sólo es viable aplicarlas a instancias del problema muy pequeñas. Las heurísticas manejan conocimiento específico del problema para obtener soluciones de alta calidad sin necesitar un excesivo esfuerzo computacional. Por otra parte, las metaheurísticas van un paso más allá, ya que son capaces de resolver una clase muy general de problemas computacionales. Finalmente, las hiperheurísticas tratan de automatizar, normalmente incorporando técnicas de aprendizaje, el proceso de selección, combinación, generación o adaptación de heurísticas más simples para resolver eficientemente problemas de optimización. Para obtener lo mejor de estos métodos se requiere conocer, además del tipo de optimización (mono o multi-objetivo) y el tamaño del problema, los medios computacionales de los que se dispone, puesto que el uso de máquinas e implementaciones paralelas puede reducir considerablemente los tiempos para obtener una solución. En las aplicaciones reales de los problemas de corte y empaquetado en la industria, la diferencia entre usar una solución obtenida rápidamente y usar propuestas más sofisticadas para encontrar la solución óptima puede determinar la supervivencia de la empresa. Sin embargo, el desarrollo de propuestas más sofisticadas y efectivas normalmente involucra un gran esfuerzo computacional, que en las aplicaciones reales puede provocar una reducción de la velocidad del proceso de producción. Por lo tanto, el diseño de propuestas efectivas y, al mismo tiempo, eficientes es fundamental. Por esta razón, el principal objetivo de este trabajo consiste en el diseño e implementación de métodos efectivos y eficientes para resolver distintos problemas de corte y empaquetado. Además, si estos métodos se definen como esquemas lo más generales posible, se podrán aplicar a diferentes problemas de corte y empaquetado sin realizar demasiados cambios para adaptarlos a cada uno. Así, teniendo en cuenta el amplio rango de metodologías de resolución de problemas de optimización y las técnicas disponibles para incrementar su eficiencia, se han diseñado e implementado diversos métodos para resolver varios problemas de corte y empaquetado, tratando de mejorar las propuestas existentes en la literatura. Los problemas que se han abordado han sido: el Two-Dimensional Cutting Stock Problem, el Two-Dimensional Strip Packing Problem, y el Container Loading Problem. Para cada uno de estos problemas se ha realizado una amplia y minuciosa revisión bibliográfica, y se ha obtenido la solución de las distintas variantes escogidas aplicando diferentes métodos de resolución: métodos exactos mono-objetivo y paralelizaciones de los mismos, y métodos aproximados multi-objetivo y paralelizaciones de los mismos. Los métodos exactos mono-objetivo aplicados se han basado en técnicas de búsqueda en árbol. Por otra parte, como métodos aproximados multi-objetivo se han seleccionado unas metaheurísticas multi-objetivo, los MOEAs. Además, para la representación de los individuos utilizados por estos métodos se han empleado codificaciones directas mediante una notación postfija, y codificaciones que usan heurísticas de colocación e hiperheurísticas. Algunas de estas metodologías se han mejorado utilizando esquemas paralelos haciendo uso de las herramientas de programación OpenMP y MPI. En el caso d

Statistical and machine learning approaches for the minimization of trigger errors in parametric earthquake catastrophe bonds

Catastrophe bonds are financial instruments designed to transfer risk of monetary losses arising from earthquakes, hurricanes, or floods to the capital markets. The insurance and reinsurance industry, governments, and private entities employ them frequently to obtain coverage. Parametric catastrophe bonds base their payments on physical features. For instance, given parameters such as magnitude of the earthquake and the location of its epicentre, the bond may pay a fixed amount or not pay at all. This paper reviews statistical and machine learning techniques for designing trigger mechanisms and includes a computational experiment. Several lines of future research are discussed

Modelling human network behaviour using simulation and optimization tools : the need for hybridization

The inclusion of stakeholder behaviour in Operations Research / Industrial Engineering (OR/IE) models has gained much attention in recent years. Behavioural and cognitive traits of people and groups have been integrated in simulation models (mainly through agent-based approaches) as well as in optimization algorithms. However, especially the influence of relations between different actors in human networks is a broad and interdisciplinary topic that has not yet been fully investigated. This paper analyses, from an OR/IE point of view, the existing literature on behaviour-related factors in human networks. This review covers different application fields, including: supply chain management, public policies in emergency situations, and Internet-based human networks. The review reveals that the methodological approach of choice (either simulation or optimization) is highly dependent on the application area. However, an integrated approach combining simulation and optimization is rarely used. Thus, the paper proposes the hybridization of simulation with optimization as one of the best strategies to incorporate human behaviour in human networks and the resulting uncertainty, randomness, and dynamism in related OR/IE models

Modelling human network behaviour using simulation and optimization tools : the need for hybridization

The inclusion of stakeholder behaviour in Operations Research / Industrial Engineering (OR/IE) models has gained much attention in recent years. Behavioural and cognitive traits of people and groups have been integrated in simulation models (mainly through agent-based approaches) as well as in optimization algorithms. However, especially the influence of relations between different actors in human networks is a broad and interdisciplinary topic that has not yet been fully investigated. This paper analyses, from an OR/IE point of view, the existing literature on behaviour-related factors in human networks. This review covers different application fields, including: supply chain management, public policies in emergency situations, and Internet-based human networks. The review reveals that the methodological approach of choice (either simulation or optimization) is highly dependent on the application area. However, an integrated approach combining simulation and optimization is rarely used. Thus, the paper proposes the hybridization of simulation with optimization as one of the best strategies to incorporate human behaviour in human networks and the resulting uncertainty, randomness, and dynamism in related OR/IE models

Horizontal cooperation practices in Internet-based Higher Education, computational logistics and telecommunications

Globalization and advances in information and communication technologies have boosted the use of Horizontal Cooperation (HC) practices in many industry sectors. In the current international market, these 'alliances' become especially relevant for small and medium organizations, which are forced to compete with large-scale corporations. This paper analyzes benefits and challenges of HC practices in three service sectors that are critical for most developed and emerging countries: Internet-based higher education, computational logistics and transportation and telecommunication services. The paper discusses the role of HC to benefit from economies of scale and reduce costs, improve quality of service and become more environmentally friendly. Hence, by using HC firms not only increase their competitiveness and extend their markets but, in addition, they also promote social responsiveness actions

Editorial of special issue on: Applications of risk analysis and analytics in engineering and economics

In areas such as engineering, economics, and insurance, real-world systems are becoming increasingly complex to analyse due to their global scale as well as to the uncertainty and dynamic conditions that characterises realistic scenarios. This increasing complexity makes risk analysis and analytic (RA&A) methods more important than ever, since being able to design, develop, and operate real-live systems while assessing and reducing their risk of malfunctions or inefficiencies constitutes one of the most relevant challenges in our current society. RA&A methods and techniques have rapidly evolved over the last years. One factor that explains this development is outstanding and continuous improvement in software and computing power, which facilitates the use of hybrid algorithms combining risk/reliability principles with modern optimisation and simulation frameworks. Another factor is the increasing use of problem solving approaches that benefit from the so-called "big data" phenomenon. However, despite these significant advances in this scientific arena, there seems to be an important gap between theory and practice; most industrial sectors (including engineering, economics, and insurance) are only starting to employ the full potential of state-of-the-art scientific advances in RA&A

A simheuristic approach for the stochastic team orienteering problem

The team orienteering problem is a variant of the well-known vehicle routing problem in which a set of vehicle tours are constructed in such in a way that: (i) the total collected reward received from visiting a subset of customers is maximized; and (ii) the length of each vehicle tour is restricted by a pre-specified limit. While most existing works refer to the deterministic version of the problem and focus on maximizing total reward, some degree of uncertainty (e.g., in customers¿ service times or in travel times) should be expected in real-life applications. Accordingly, this paper proposes a simheuristic algorithm for solving the stochastic team orienteering problem, where goals other than maximizing the expected reward need to be considered. A series of numerical experiments contribute to illustrate the potential of our approach, which integrates Monte Carlo simulation inside a metaheuristic framework

Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

The uncapacitated facility location problem (UFLP) is a popular combinatorial optimization problem with practical applications in different areas, from logistics to telecommunication networks. While most of the existing work in the literature focuses on minimizing total cost for the deterministic version of the problem, some degree of uncertainty (e.g., in the customers' demands or in the service costs) should be expected in real-life applications. Accordingly, this paper proposes a simheuristic algorithm for solving the stochastic UFLP (SUFLP), where optimization goals other than the minimum expected cost can be considered. The development of this simheuristic is structured in three stages: (i) first, an extremely fast savings-based heuristic is introduced; (ii) next, the heuristic is integrated into a metaheuristic framework, and the resulting algorithm is tested against the optimal values for the UFLP; and (iii) finally, the algorithm is extended by integrating it with simulation techniques, and the resulting simheuristic is employed to solve the SUFLP. Some numerical experiments contribute to illustrate the potential uses of each of these solving methods, depending on the version of the problem (deterministic or stochastic) as well as on whether or not a real-time solution is required

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.Este artículo revisa la literatura existente sobre la combinación de metaheurísticas con métodos de aprendizaje automático y luego introduce el concepto de heurística de aprendizaje, un tipo novedoso de algoritmos híbridos. Las técnicas de aprendizaje se pueden usar para resolver problemas combinatorios de optimización con entradas dinámicas (COPDI). En estos COPDI, las entradas problemáticas (elementos ubicados ya sea en la función objetivo o en el conjunto de restricciones) no se fijan de antemano como de costumbre. Por el contrario, pueden variar de forma predecible (no aleatoria) ya que la solución se construye parcialmente de acuerdo con algún proceso iterativo basado en heurística. Por ejemplo, la disposición de un consumidor a gastar en un producto específico puede cambiar a medida que disminuye la disponibilidad de este producto y aumenta su precio. Por lo tanto, estas entradas pueden tomar diferentes valores dependiendo de la configuración de la solución actual. Estas variaciones en las entradas pueden requerir una coordinación entre el mecanismo de aprendizaje y el algoritmo metaheurístico: en cada iteración, el método de aprendizaje actualiza el modelo de entradas utilizado por la metaheurística.Aquest article revisa la literatura existent sobre la combinació de metaheurístiques amb mètodes d'aprenentatge automàtic i després introdueix el concepte d'heurística d'aprenentatge, un tipus nou d'algorismes híbrids. Les tècniques d'aprenentatge es poden usar per resoldre problemes combinatoris d'optimització amb entrades dinàmiques (COPDI). En aquests COPDI, les entrades problemàtiques (elements ubicats ja sigui en la funció objectiu o en el conjunt de restriccions) no es fixen per endavant com de costum. Per contra, poden variar de forma predictible (no aleatòria) ja que la solució es construeix parcialment d'acord amb algun procés iteratiu basat en heurística. Per exemple, la disposició d'un consumidor a gastar en un producte específic pot canviar a mesura que disminueix la disponibilitat d'aquest producte i augmenta el preu. Per tant, aquestes entrades poden prendre diferents valors depenent de la configuració de la solució actual. Aquestes variacions en les entrades poden requerir una coordinació entre el mecanisme d'aprenentatge i l'algoritme metaheurístic: en cada iteració, el mètode d'aprenentatge actualitza el model d'entrades utilitzat per la metaheurística

Editorial of special issue on: Applications of risk analysis and analytics in engineering and economics

In areas such as engineering, economics, and insurance, real-world systems are becoming increasingly complex to analyse due to their global scale as well as to the uncertainty and dynamic conditions that characterises realistic scenarios. This increasing complexity makes risk analysis and analytic (RA&A) methods more important than ever, since being able to design, develop, and operate real-live systems while assessing and reducing their risk of malfunctions or inefficiencies constitutes one of the most relevant challenges in our current society. RA&A methods and techniques have rapidly evolved over the last years. One factor that explains this development is outstanding and continuous improvement in software and computing power, which facilitates the use of hybrid algorithms combining risk/reliability principles with modern optimisation and simulation frameworks. Another factor is the increasing use of problem solving approaches that benefit from the so-called "big data" phenomenon. However, despite these significant advances in this scientific arena, there seems to be an important gap between theory and practice; most industrial sectors (including engineering, economics, and insurance) are only starting to employ the full potential of state-of-the-art scientific advances in RA&A