OR models in urban service facility location : a critical review of applications and future developments

[EN] Facility location models are well established in various application areas with more than a century of history in academia. Since the 1970s the trend has been shifting from manufacturing to service industries. Due to their nature, service industries are frequently located in or near urban areas that results in additional assumptions, objectives and constraints other than those in more traditional manufacturing location models. This survey focuses on the location of service facilities in urban areas. We studied 110 research papers across different journals and disciplines. We have analyzed these papers on two levels. On the first, we take an Operations Research perspective to investigate the papers in terms of types of decisions, location space, main assumptions, input parameters, objective functions and constraints. On the second level, we compare and contrast the papers in each of these applications categories: (a) Waste management systems (WMS), (b) Large-scale disaster (LSD), (c) Small-scale emergency (SSE), (d) General service and infrastructure (GSI), (e) Non-emergency healthcare systems (NEH) and (f) Transportation systems and their infrastructure (TSI). Each of these categories is critically analyzed in terms of application, assumptions, decision variables, input parameters, constraints, objective functions and solution techniques. Gaps, research opportunities and trends are identified within each category. Finally, some general lessons learned based on the practicality of the models is synthesized to suggest avenues of future research.Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards (No. DPI2015-65895-R) financed by FEDER funds.Farahani, RZ.; Fallah, S.; Ruiz García, R.; Hosseini, S.; Asgari, N. (2019). OR Models in Urban Service Facility Location: A Critical Review of Applications and Future Developments. European Journal of Operational Research. 276(1):1-27. https://doi.org/10.1016/j.ejor.2018.07.036S127276

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Advanced meta-heuristic approaches and their application to operational optimization in forest wildfire management

La última década ha sido testigo de un aumento vertiginoso de la cantidad y frecuencia de desastres a gran escala, principalmente debido a los fenómenos devastadores derivados de paradigmas climatológicos y ambientales a gran escala como el calentamiento global. De entre ellos son las inundaciones, huracanes y terremotos los desastres de mayor frecuencia de aparición y fatales consecuencias durante este período, tal como certifican los más de 20.000 muertos a consecuencia de un terremoto en la región de Gujarat (India) en 2001, o las 230.000 y 316.000 pérdidas humanas de los terremotos de Indonesia y Haití en 2004 y 2010, respectivamente. En este contexto, el enfoque de esta tesis se centra en una casuística concreta de desastre a media-gran escala cuya frecuencia y severidad han crecido de manera igualmente preocupante en los últimos tiempos: los incendios, definidos como un fuego de grandes dimensiones no voluntariamente iniciado por el ser humano, y que afecta a aquello que no está destinado a quemarse. Pese a la diversidad de iniciativas, campañas y procedimientos orientados a la minimización del impacto y las consecuencias de los incendios, varios sucesos fatales acontecidos en los últimos años han puesto en duda la efectividad de las políticas actuales de gestión de recursos contra incendios como aeronaves, vehículos terrestres, equipamiento de comunicaciones radio, logística de abastecimiento y las brigadas desplegadas en el área afectada. Un ejemplo manifiesto de esta falta de eficacia es la muerte de once bomberos ocurrida en un incendio de 130 kilómetros cuadrados en la zona de Guadalajara (España) en 2005, oficialmente atribuida a una deficiente coordinación entre el puesto de mando y los equipos de extinción debida, fundamentalmente, a problemas de cobertura en los sistemas de radiocomunicación. Aunque la causa de esta falta de coordinación ha sido cuestionada por las autoridades y los agentes involucrados desde entonces, lo cierto es que este suceso supone un ejemplo evidente de la necesidad de estudiar y desarrollar herramientas algorítmicas que ayuden al personal de comandancia a ejecutar óptimamente sus tareas de coordinación y control. Desafortunadamente la coyuntura de crisis económica mundial que azota con especial fuerza los países del Sur de Europa ha mermado dramáticamente las partidas presupuestarias para la prevención y extinción de incendios en beneficio de programas nacionales de reducción de déficit. A consecuencia de estos recortes, el coste ha irrumpido con fuerza como un criterio de extrema relevancia en la planificación operativa de este tipo de desastres: desde la perspectiva de un problema de optimización, los recursos contra incendios son actualmente gestionados con el objetivo fundamental de maximizar su efectividad contra incendios, sujeto a la restricción de que el coste agregado asociado a las decisiones tomadas no supere un determinado umbral presupuestario. Pese a que estas restricciones de coste están bien acotadas, en la práctica la mayoría de los procedimientos de gestión de recursos contra incendios están fuertemente determinados por la capacidad limitada del ser humano para tomar decisiones ágiles en escenarios de elevada complejidad y heterogeneidad. Por los motivos anteriormente expuestos, la presente Tesis doctoral propone la adopción de algoritmos meta-heurísticos para solventar eficientemente problemas de optimización que modelan procesos de gestión de recursos contra incendios. Esta familia de algoritmos de optimización es capaz de explorar el espacio solución de un problema dado merced a la aplicación iterativa de mecanismos inteligentes de búsqueda explorativa y explotativa, produciendo soluciones que sacrifican calidad por una complejidad computacional menor en comparación con la resultante de procesos determinísticos de búsqueda exhaustiva. En particular la Tesis plantea la búsqueda por harmonía (del inglés Harmony Search) como la técnica meta-heurística de optimización común a las herramientas diseñadas para la gestión de recursos en dos escenarios diferentes: ? El primer escenario analizado contempla el despliegue óptimo de redes de comunicación inalámbrica para la coordinación de equipos de extinción en incendios forestales de gran escala. Desde el punto de vista formal, el problema del despliegue dinámico de retransmisores que caracteriza matemáticamente este escenario consiste en estimar el número y localización de los retransmisores radio que deben ser desplegados en el área afectada por el incendio, de tal modo que el número de nodos móviles (i.e. recursos) con cobertura radio es maximizado a un coste del despliegue mínimo. A fin de reflejar la diversidad de equipamiento de retransmisión radio existente en la realidad, este problema es reformulado para considerar modelos de retransmisor con diferentes características de cobertura y coste. El problema resultante es resuelto de manera eficiente mediante sendos algoritmos mono- y bi-objetivo que conjugan 1) la Búsqueda por Harmonía como método de búsqueda global; y 2) una versión modificada del algoritmo de agrupación K-means como técnica de búsqueda local. El desempeño de los métodos propuestos es evaluado mediante experimentos numéricos basados en datos estadísticos reales de la Comunidad de Castilla la Mancha (España), merced a cuyos resultados queda certificada su practicidad a la hora de desplegar infraestructura de comunicación en este tipo de desastres. ? El segundo escenario bajo estudio se concentra en el despliegue y planificación óptima de vehículos aéreos de extinción de incendios basados en estimaciones predictivas del riesgo de incendio de una cierta área geográfica. De manera enunciativa, el problema subyacente busca la asignación de recursos a aeródromos y aeropuertos con restricciones de capacidad que maximice la utilidad de dichos recursos en relación al riesgo de incendio y minimice, a su vez, el coste de ejecutar dicha asignación. La formulación de este problema también considera, dentro de la definición de dicha función de utilidad, la distancia relativa entre aeropuerto, punto de potencial riesgo de incendio y el recurso acuífero (lago, río o mar) más cercano. Para su resolución eficiente se propone el uso de algoritmos de optimización basados, de nuevo, en la Búsqueda por Harmonía, incorporando además métodos voraces de reparación capacitiva. La aplicabilidad práctica de estos métodos es validada mediante experimentos numéricos en escenarios sintéticos y un caso práctico que incluye valores reales del riesgo de incendio, posiciones de recursos acuíferos e instalaciones aeroportuarias. En resumen, esta Tesis evidencia, desde un punto de vista práctico, que la meta-heurística moderna supone una solución algorítmica computacionalmente eficiente para tratar problemas de gestión de recursos contra incendios sujetos a restricciones de coste

Spatially optimised sustainable urban development

PhD ThesisTackling urbanisation and climate change requires more sustainable and resilient cities, which in turn will require planners to develop a portfolio of measures to manage climate risks such as flooding, meet energy and greenhouse gas reduction targets, and prioritise development on brownfield sites to preserve greenspace. However, the policies, strategies and measures put in place to meet such objectives can frequently conflict with each other or deliver unintended consequences, hampering long-term sustainability. For example, the densification of cities in order to reduce transport energy use can increase urban heat island effects and surface water flooding from extreme rainfall events. In order to make coherent decisions in the presence of such complex multi-dimensional spatial conflicts, urban planners require sophisticated planning tools to identify and manage potential trade-offs between the spatial strategies necessary to deliver sustainability. To achieve this aim, this research has developed a multi-objective spatial optimisation framework for the spatial planning of new residential development within cities. The implemented framework develops spatial strategies of required new residential development that minimize conflicts between multiple sustainability objectives as a result of planning policy and climate change related hazards. Five key sustainability objectives have been investigated, namely; (i) minimizing risk from heat waves, (ii) minimizing the risk from flood events, (iii) minimizing travel costs in order to reduce transport emissions, (iv) minimizing urban sprawl and (v) preventing development on existing greenspace. A review identified two optimisation algorithms as suitable for this task. Simulated Annealing (SA) is a traditional optimisation algorithm that uses a probabilistic approach to seek out a global optima by iteratively assessing a wide range of spatial configurations against the objectives under consideration. Gradual ‘cooling’, or reducing the probability of jumping to a different region of the objective space, helps the SA to converge on globally optimal spatial patterns. Genetic Algorithms (GA) evolve successive generations of solutions, by both recombining attributes and randomly mutating previous generations of solutions, to search for and converge towards superior spatial strategies. The framework works towards, and outputs, a series of Pareto-optimal spatial plans that outperform all other plans in at least one objective. This approach allows for a range of best trade-off plans for planners to choose from. ii Both SA and GA were evaluated for an initial case study in Middlesbrough, in the North East of England, and were able to identify strategies which significantly improve upon the local authority’s development plan. For example, the GA approach is able to identify a spatial strategy that reduces the travel to work distance between new development and the central business district by 77.5% whilst nullifying the flood risk to the new development. A comparison of the two optimisation approaches for the Middlesbrough case study revealed that the GA is the more effective approach. The GA is more able to escape local optima and on average outperforms the SA by 56% in in the Pareto fronts discovered whilst discovering double the number of multi-objective Pareto-optimal spatial plans. On the basis of the initial Middlesbrough case study the GA approach was applied to the significantly larger, and more computationally complex, problem of optimising spatial development plans for London in the UK – a total area of 1,572km2. The framework identified optimal strategies in less than 400 generations. The analysis showed, for example, strategies that provide the lowest heat risk (compared to the feasible spatial plans found) can be achieved whilst also using 85% brownfield land to locate new development. The framework was further extended to investigate the impact of different development and density regulations. This enabled the identification of optimised strategies, albeit at lower building density, that completely prevent any increase in urban sprawl whilst also improving the heat risk objective by 60% against a business as usual development strategy. Conversely by restricting development to brownfield the ability of the spatial plan to optimise future heat risk is reduced by 55.6% against the business as usual development strategy. The results of both case studies demonstrate the potential of spatial optimisation to provide planners with optimal spatial plans in the presence of conflicting sustainability objectives. The resulting diagnostic information provides an analytical appreciation of the sensitivity between conflicts and therefore the overall robustness of a plan to uncertainty. With the inclusion of further objectives, and qualitative information unsuitable for this type of analysis, spatial optimization can constitute a powerful decision support tool to help planners to identify spatial development strategies that satisfy multiple sustainability objectives and provide an evidence base for better decision making

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Recent Experiences in Multidisciplinary Analysis and Optimization, part 2

The papers presented at the NASA Symposium on Recent Experiences in Multidisciplinary Analysis and Optimization held at NASA Langley Research Center, Hampton, Virginia, April 24 to 26, 1984 are given. The purposes of the symposium were to exchange information about the status of the application of optimization and the associated analyses in industry or research laboratories to real life problems and to examine the directions of future developments