From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

The use of direct current distribution systems in delivering scalable charging infrastructure for battery electric vehicles

The use of low voltage direct current (LVDC) distribution is becoming recognised as a technology enabler that can be used to efficiently network native DC generators with DC loads, offer improved power sharing capabilities, reduce power system material resource requirements and enhance the performance of variable speed machinery. Practical deployment opportunities for LVDC range from small-scale microgrids in the context of energy for development to sophisticated, modern building-level power distribution systems for commercial office spaces, manufacturing applications and industrial processes. However, the incumbent AC distribution system benefits from existing technical product and safety standards, which makes the early adoption of LVDC systems challenging from a risk and cost perspective. Concurrently, the demand for native DC loads such as Battery Electric Transportation Systems is growing. This is especially significant in the area of private electric vehicles (EVs), taxis and buses, but the prospect of electric trucks, ferries and shortrange aircraft are also tangible opportunities. The success of this electric transport revolution depends on several factors, one of which is the availability of battery charging infrastructure that can cost effectively integrate with the existing electrical network, deliver adequate energy transfer rates and adapt to the rapid technical development of this industry. This thesis explores the application of two, novel LVDC distribution systems for the development of scalable EV charging networks; where charging infrastructure has the ability to scale with increasing EV adoption and has a lower risk of becoming a stranded asset in the future. The modelling is supported by real, rapid DC charger utilisation data from the national charging network in Scotland, comprising over 192 chargers and 400,000 charging events. During the work of this thesis, it was found that a combined heat and power (CHP) system can economically support short duration charging scenarios by providing additional power capacity in a congested electrical grid. In this case the highest system efficiency and Net Present Value (NPV) is achieved with a fuel cell directly connected to the DC charging network, compared to other gas reciprocating CHP options. Furthermore, the proposition of a reconfigurable LVDC charging network, interfaced to the public AC distribution network, reduces the capital outlay, offers a higher NPV and improved scalability compared to other charging solutions. For charging system designers and operators, it was found that rapid DC chargers can be classified by specific locations, each possessing a distinct Gaussian arrival pattern and Gamma distribution for charging energy delivered.The use of low voltage direct current (LVDC) distribution is becoming recognised as a technology enabler that can be used to efficiently network native DC generators with DC loads, offer improved power sharing capabilities, reduce power system material resource requirements and enhance the performance of variable speed machinery. Practical deployment opportunities for LVDC range from small-scale microgrids in the context of energy for development to sophisticated, modern building-level power distribution systems for commercial office spaces, manufacturing applications and industrial processes. However, the incumbent AC distribution system benefits from existing technical product and safety standards, which makes the early adoption of LVDC systems challenging from a risk and cost perspective. Concurrently, the demand for native DC loads such as Battery Electric Transportation Systems is growing. This is especially significant in the area of private electric vehicles (EVs), taxis and buses, but the prospect of electric trucks, ferries and shortrange aircraft are also tangible opportunities. The success of this electric transport revolution depends on several factors, one of which is the availability of battery charging infrastructure that can cost effectively integrate with the existing electrical network, deliver adequate energy transfer rates and adapt to the rapid technical development of this industry. This thesis explores the application of two, novel LVDC distribution systems for the development of scalable EV charging networks; where charging infrastructure has the ability to scale with increasing EV adoption and has a lower risk of becoming a stranded asset in the future. The modelling is supported by real, rapid DC charger utilisation data from the national charging network in Scotland, comprising over 192 chargers and 400,000 charging events. During the work of this thesis, it was found that a combined heat and power (CHP) system can economically support short duration charging scenarios by providing additional power capacity in a congested electrical grid. In this case the highest system efficiency and Net Present Value (NPV) is achieved with a fuel cell directly connected to the DC charging network, compared to other gas reciprocating CHP options. Furthermore, the proposition of a reconfigurable LVDC charging network, interfaced to the public AC distribution network, reduces the capital outlay, offers a higher NPV and improved scalability compared to other charging solutions. For charging system designers and operators, it was found that rapid DC chargers can be classified by specific locations, each possessing a distinct Gaussian arrival pattern and Gamma distribution for charging energy delivered