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

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

Optimizing an integrated home care problem: a heuristic-based decision-support system

With the continuous increase in longevity worldwide, the elderly population requiring home health and social care has been continuously growing over the years. Planning combined home health and social services has been shown to be a very difficult task for current decision-makers, not only due to the high number of working regulations and user-related necessities that need to be considered but also due to the need for synchronizing both types of services. Moreover, it is highly desirable that users are visited by the fewest number of different caregivers in the same kind of appointments (continuity of service). The complex and multi-objective nature of the synchronized home health and social care routing and scheduling problem has called for the development of automated planning systems that are able to obtain efficient solutions in reasonable computational times. In this work, we propose two heuristic methods to optimize routing and scheduling decisions for this problem with an extensive set of constraints and objectives. We use (real) data and information from current care providers in the Barcelona area to build and test our models and provide insights into parameter tuning and the trade-off between the associated operating costs, continuity of service and number of unscheduled services. The proposed tool is made available via a web-based decision support system that allows decision-makers to obtain efficient solutions in an intuitive, complete, and timely manner.This work has been supported by the Ministry of Science, Innovation and Universities of the Government of Spain (Ministerio de Ciencia, Inovacion Universidades (MCIU), Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarollo Regional (FEDER)) (RTI2018-095197-B-I00) and “la Caixa” Banking Foundation under the project code LCF/PR/SR19/52540014

Optimization of the real-time response to roadside incidents through heuristic and linear programming

This paper presents a solution for a real-world roadside assistance problem. Roadside incidents can happen at any time. Depending on the type of incident, a specific resource from the roadside assistance company can be sent on site. The problem of allocating resources to these road-side incidents can be stated as a multi-objective function and a large set of constraints, including priorities and preferences, resource capacities and skills, calendars, and extra hours. The request from the client is to a have real-time response and to attempt to use only open source tools. The optimization objectives to consider are the minimization of the operational costs and the minimization of the time to arrive to each incident. In this work, an innovative approach to near-optimally solving this problem in real-time is proposed, combining a heuristic approach and linear programming. The results show the great potential of this approach: operational costs were reduced by 19%, the use of external providers was reduced to half, and the productivity of the resources owned by the client was significantly increased

Improving the accessibility to public schools in urban areas of developing countries through a location model and an analytical framework

The location of primary public schools in urban areas of developing countries is the focus of this study. In such areas, new schools and modification of the current schools are required, and this process should be developed using rational and broad supporting tools for decision makers, such as optimization models. We propose a realistic coverage location model and a framework to analyze the location of schools. Our approach considers the existing schools and their resizing, the best locations of the new schools that may have different capacities, population coverage, walking distances and budget provisions for building and updating schools. As a case study, we assess the current primary school network in Ciudad Benito Juarez to provide managerial insights. Through the proposed framework, we analyze the current locations of schools and decisions to be made by considering future scenarios in different time periods. The proposed model is quite flexible and easy to adapt to new considerations, allowing it to be applied to regions in developing countries under similar conditions.Ministerio de ciencia, innovación y universidades Award Number: RTI2018-095197-B-I00 Recipient: Helena Ramalhinho, Jesica de Armas The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Agència de gestió d’ajuts universitaris i de recerca Award Number: 2017-SGR-1739 Recipient: Helena Ramalhinho, Jesica de Armas The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Departament de Recerca i Universitats (Generalitat de Catalunya) Award Number: 2020 Pandemies 00090. Recipient: Marta Reynal-Querol The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Ministerio de ciencia e innovación Award Number: PID2020-120118GB-I00 Recipient: Marta Reynal-Querol The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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

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

Optimizing assistive technology operations for aging populations

The combination of increasing life spans and low birth rates is accelerating the pace at which the share of older adults in the population worldwide is rising. As people age, their autonomy tends to decrease which leads frequently to the need to use support equipment to perform their daily living activities. Governments, at all levels, are establishing programs to enable this population to live with dignity at home, receive more proper care, and to participate in all life’s activities in a joyful and independent way. Within these programs, we can find the Assistive Technology (AT) organizations that makes available to the population assistive technology equipment as wheelchairs or hospital beds. These organizations collect and store donated products for lend them to needed users. The management of these products’ flow, the location of the access centers, and design of the transportation schemes is not straightforward, due to several complexities, such as a highly uncertain demand and offer, budget limitations, and restricted availability of human resources within the organizations, most of which are volunteers. In this paper, we analyze the AT operations, based on a Circular Economy perspective, and we develop tools that can help the managers of these programs to make better logistics decisions. These tools are based on mathematical models and efficient algorithms that have been developed to solve location, inventory, and routing operational problems in the AT organizations. We have been inspired by the social program of the Barcelona City Council, Banc del Moviment, but the tools can be used and extended to other programs around the world.This research has been supported by the Ministry of Science, Innovation and Universities of the Government of Spain (Ministerio de Ciencia, Innovación y Universidades (MCIU), Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER))(RTI2018-095197-B-I00) and “la Caixa” Banking Foundation under the project code LCF/PR/SR19/52540014