Planning and reconfigurable control of a fleet of unmanned vehicles for taxi operations in airport environment

The optimization of airport operations has gained increasing interest by the aeronautical community, due to the substantial growth in the number of airport movements (landings and take-offs) experienced in the past decades all over the world. Forecasts have confirmed this trend also for the next decades. The result of the expansion of air traffic is an increasing congestion of airports, especially in taxiways and runways, leading to additional amount of fuel burnt by airplanes during taxi operations, causing additional pollution and costs for airlines. In order to reduce the impact of taxi operations, different solutions have been proposed in literature; the solution which this dissertation refers to uses autonomous electric vehicles to tow airplanes between parking lots and runways. Although several analyses have been proposed in literature, showing the feasibility and the effectiveness of this approach in reducing the environmental impact, at the beginning of the doctoral activity no solutions were proposed, on how to manage the fleet of unmanned vehicles inside the airport environment. Therefore, the research activity has focused on the development of algorithms able to provide pushback tractor (also referred as tugs) autopilots with conflict-free schedules. The main objective of the optimization algorithms is to minimize the tug energy consumption, while performing just-in-time runway operations: departing airplanes are delivered only when they can take-off and the taxi-in phase starts as soon as the aircraft clears the runway and connects to the tractor. Two models, one based on continuous time and one on discrete time evolution, were developed to simulate the taxi phases within the optimization scheme. A piecewise-linear model has also been proposed to evaluate the energy consumed by the tugs during the assigned missions. Furthermore, three optimization algorithms were developed: two hybrid versions of the particle swarm optimization and a tree search heuristic. The following functional requirements for the management algorithm were defined: the optimization model must be easily adapted to different airports with different layout (reconfigurability); the generated schedule must always be conflict-free; and the computational time required to process a time horizon of 1h must be less than 15min. In order to improve its performance, the particle swarm optimization was hybridized with a hill-climb meta-heuristic; a second hybridization was performed by means of the random variable search, an algorithm of the family of the variable neighborhood search. The neighborhood size for the random variable search was considered varying with inverse proportionality to the distance between the actual considered solution and the optimal one found so far. Finally, a tree search heuristic was developed to find the runway sequence, among all the possible sequences of take-offs and landings for a given flight schedule, which can be realized with a series of taxi trajectories that require minimum energy consumption. Given the taxi schedule generated by the aforementioned optimization algorithms a tug dispatch algorithm, assigns a vehicle to each mission. The three optimization schemes and the two mathematical models were tested on several test cases among three airports: the Turin-Caselle airport, the Milan-Malpensa airport, and the Amsterdam airport Schiphol. The cost required to perform the generated schedules using the autonomous tugs was compared to the cost required to perform the taxi using the aircraft engines. The proposed approach resulted always more convenient than the classical one

Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics

242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro

A Systematic Review for Transformer-based Long-term Series Forecasting

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field

Heuristics for New Problems Arising in the Transport of People and Goods

The Vehicle Routing Problem (VRP) and its numerous variants are amongst the most widely studied in the entire Operations Research literature, with applications in fields includ- ing supply chain management, journey planning and vehicle scheduling. In this thesis, we focus on three problems from two fields with a wide reach; the design of public trans- port systems and the robust routing of delivery vehicles. Each chapter investigates a new setting, formulates an optimization problem, introduces various solution methods and presents computational experiments highlighting salient points. The first problem involves commuters who use a flexible shuttle service to travel to a main transit hub, where they catch a fixed route public transport service to their true destina- tion. In our variant, passengers must forgo some of the choices they had in previous ver- sions; the service provider chooses the specific hub passengers are taken to (provided all relevant timing constraints are satisfied). This introduces both complexities and opportu- nities not seen in other VRP variants, so we present two solution methods tailored for this problem. An extensive computational study over a range of networks shows this flexibility allows significant cost savings with little impact on the quality of service received. The second problem involves dynamic ridesharing schemes and one of their most per- sistent drawbacks: the requirement to attract a large number of users during the start up phase. Although this is influenced by many factors, a significant consideration is the per- ceived uncertainty around finding a match. To address this, the service provider may wish to employ a small number of their own private drivers, to serve riders who would oth- erwise remain unmatched. We explore how this could be formulated as an optimization problem and discuss the objectives and constraints the service provider may have. We then describe a special structure inherent to the problem and present three different so- lution methods which exploit this. Finally, a broad computational study demonstrates the potential benefits of these dedicated drivers and identifies environments in which they are most useful. The third problem comes from the field of logistics and involves a large delivery firm serving an uncertain customer set. The firm wishes to build low cost delivery routes that remain efficient after the appearance and removal of some customers. We formulate this problem and present a heuristic which is both computationally cheaper and more versatile than comparative exact methods. A wide computational study illustrates our heuristic’s predictive power and its efficacy compared to natural alternative strategies

Design of Heuristic Algorithms for Hard Optimization

This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Extracting Computational Representations of Place with Social Sensing

Place-based GIS are at the forefront of GIScience research and characterized by textual descriptions, human conceptualizations as well as the spatial-semantic relationships among places. The concepts of places are difficult to handle in geographic information science and systems because of their intrinsic vagueness. They arise from the complex interaction of individuals, society, and the environment. The exact delineation of vague regions is challenging as their borders are vague and the membership within a region varies non-monotonically and as a function of context. Consequently, vague regions are difficult to handle computationally, e.g., in spatial analysis, cartography, geographic information retrieval, and GIS workflows in general. The emergence of big data brings new opportunities for us to understand the place semantics from large-scale volunteered geographic information and data streams, such as geotags, texts, activity streams, and GPS trajectories. The term "social sensing" describes such individual-level big geospatial data and the associated analysis methods. In this dissertation, I present a generalizable, data-driven framework that complements classical top-down approaches by extracting the representations of vague cognitive regions and function regions from bottom-up approaches using spatial statistics and machine learning techniques with various social sensing sources. I demonstrate how to derive crisp boundaries for cognitive and functional regions from points of interest data, and show how natural language processing techniques can enrich our understanding of places and form a foundation for the semantic characterization of place types and the generalization of regions. This work makes contributions to the development of computational methodologies for extracting vague cognitive regions and functional regions using data-driven approaches as well as the novel semantic generalization processing technique

From efficiency to reduction

This book presents the results of the international research project CODALoop: Community Data Loop for Energy Conscious Lifestyles. It dissects the energy practices that make urban households demanding energy in their daily life and reveals the pathway towards reducing this energy demand. To unpack energy practices, the authors of this volume move away from efficiency problems studying the interaction between human and new technologies. Instead, they use a repertoire of different analytical instruments to study how interaction between humans, and between humans and data, change the social norms that shape energy needs. The volume offers a synthesis of a cross- disciplinary study of energy reduction carried out in three different countries through multiple methodological approaches. The project at the source of the book was funded under the Joint Program Initiative 'Urban Europe' and the ERA-net framework. To unpack energy practices, the authors of this volume move away from efficiency problems studying the interaction between human and new technologies. Instead, they use a repertoire of different analytical instruments to study how interaction between humans, and between humans and data, change the social norms that shape energy needs. The volume offers a synthesis of a cross- disciplinary study of energy reduction carried out in three different countries through multiple methodological approaches. The project at the source of the book was funded under the Joint Program Initiative 'Urban Europe' and the ERA-net framework