Swerve

Carpooling yields great benefits environmentally, socially, and economically for carpooling, however there is no easy to use, safe, and enjoyable application for people to connect with others who are both close in proximity and have schedules that match currently. By creating a database and visual mock ups, our senior project creates the basis for an application called Swerve that matches users by location and schedules and has social and economic incentives. Our research allowed us to further understand the social, environmental and economic benefits and incentives of carpooling. We also looked into current carpooling websites and applications and could not find a successful platform for carpooling that involves both matching and social profile components. Through surveys and interviews we confirmed our belief that there is a great student interest in a social carpooling application as well as gain an understanding of what users would want and need in the application. Based off of all of this knowledge we were able to build an Access database that matches drivers and passengers based off of location and schedules and a visual mock up of the application screens that show how the social matching would work

Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process

Efficient Solution of Minimum Cost Flow Problems for Large-scale Transportation Networks

With the rapid advance of information technology in the transportation industry, of which intermodal transportation is one of the most important subfields, the scale and dimension of problem sizes and datasets is rising significantly. This trend raises the need for study on improving the efficiency, profitability and level of competitiveness of intermodal transportation networks while exploiting the rich information of big data related to these networks. Therefore, this dissertation aims to investigate intermodal transportation network design problems, especially practical optimization problems, and to develop more realistic and effective models and solution approaches that will assist network operators and/or decision makers of the intermodal transportation system. This dissertation focuses on developing a novel strategy for solving the Minimum Cost Flow (MCF) problem for large-scale network design problems by adopting a divide-and-conquer policy during the optimization process. The main contribution is the development of an agglomerative clustering based tiling strategy to significantly reduce the computational and peak memory consumption of the MCF model for large-scale networks. The tiling strategy is supported by the regional-division theorem and -approximation regional-division theorem that are proposed and proved in this dissertation. The region-division theorem is a sufficient condition to exactly guarantee the consistency between the local MCF solution of each sub-network obtained by the aforementioned tiling strategy and the global MCF solution of the whole network. Furthermore, the -approximation region-division theorem provides worst-case bounds, so that the practical approximation MCF solution closely approximates the optimal solution in terms of its optimal value. A series of experiments are performed to evaluate the utility of the proposed approach of solving the large-scale MCF problem. The results indicate that the proposed approach is beneficial to save the execution time and peak memory consumption in large-scale MCF problems under different circumstances

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

The dial-a-ride problem with electric vehicles and battery swapping stations

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for customers with special needs and/or disabilities. The DARP with Electric Vehicles and battery swapping stations (DARP-EV) concerns scheduling a fleet of EVs to serve a set of pre-specified transport requests during a certain planning horizon. In addition, EVs can be recharged by swapping their batteries with charged ones from any battery-swap stations. We propose three enhanced Evolutionary Variable Neighborhood Search (EVO-VNS) algorithms to solve the DARP-EV. Extensive computational experiments highlight the relevance of the problem and confirm the efficiency of the proposed EVO-VNS algorithms in producing high quality solutions

Optimización multiobjetivo del transporte de personas discapacitadas: diseño de nuevas metodologías metaheurísticas

La investigación desarrolla una herramienta metodológica basada en técnicas metaheurísticas para resolver el problema de optimización del diseño de rutas para trasladar a personas de movilidad reducida, de avanzada edad o con algún tipo de discapacidad, dirigiendo la aplicabilidad de los resultados del estudio al sector público local y regional. Se formula el modelo ajustando el Dial-A-Ride Problem (DARP) estático sin ventanas de tiempo con flota fija de vehículos adaptados. La metodología propuesta para su resolución pasa por el empleo de MultiObjective Adaptative Memory Procedure (MOAMP) con incorporación de estrategias de aceleración ad-hoc. El modelo se resuelve bajo un enfoque biobjetivo, con un objetivo de carácter económico y otro, muy importante, de carácter social. Éste consigue mejorar la calidad del servicio de transporte minimizando el tiempo excedente de viaje para el usuario de movilidad reducida, que, se reconoce, presenta más sensibilidad a este factor que otros colectivos de la sociedad

Ferramenta de suporte ao projeto de sistemas flexíveis de transporte público de passageiros

Tese de Doutoramento em Engenharia Industrial e de Sistemas.As áreas rurais, com densidades populacionais baixas, apresentam desafios à mobilidade das suas populações. Os serviços de transporte público regular têm-se mostrado ineficazes e ineficientes levando os operadores de transporte coletivo a reduzir a sua oferta e a diminuir a qualidade do serviço oferecido. Em alternativa aos serviços regulares de transporte, alguns estudos têm vindo a mostrar as vantagens da implementação de sistemas de transportes flexíveis, em particular, transportes a pedido (DRT - Demand Responsive Transport). No entanto, os principais resultados observados nos estudos realizados apontam para a existência de várias dificuldades para o sucesso dos DRTs (aspetos legais, organizacionais, financeiros, etc.), assim como para a inexistência de ferramentas de apoio capazes de auxiliar os decisores nas etapas do planeamento estratégico e tático, antes mesmo de proceder à sua implementação. No sentido de minorar ou colmatar as lacunas referidas, esta tese pretende contribuir para uma discussão abrangente destes sistemas de transporte e propor uma nova ferramenta de suporte ao projeto de sistemas DRT. A ferramenta proposta integra um sistema de apoio à decisão (SAD) concebido para estimar o desempenho operacional de diferentes configurações a implementar, permitindo optar pela melhor solução encontrada. O SAD é suportado por um modelo de simulação microscópica do funcionamento do sistema, e inclui métodos de solução para diferentes variantes do problema de otimização de rotas e escalas encontradas neste tipo de serviços de transporte, para além de uma framework para a avaliação da sustentabilidade das soluções. Na validação do SAD desenvolvido, utilizou-se um estudo de caso português. Os resultados dos testes efetuados permitiram evidenciar as potencialidades da ferramenta proposta. Adicionalmente, a avaliação da sustentabilidade da solução permitiu identificar a difícil sustentabilidade financeira deste tipo de sistemas, mas também as suas vantagens em termos sociais e ambientais que poderão justificar a sua adoção.Rural areas with low population densities, present challenges to mobility of their populations. The regular public transport services have proved quite ineffective and inefficient leading transport operators to reduce their supply and their services quality. As an alternative to regular services, some studies have come to show the advantages of the implementation of flexible transport systems, as demand responsive transport (DRT). However, the main results obtained in the studies scope point both to the existence of different types of difficulties to the DRTs success (legal, organizational, financial aspects, etc.), and the lack of supporting tools that can assist decision makers in both strategic and tactical planning, even before proceeding to implement. In order to overcome these shortcomings, this thesis intends to discuss broadly these transport systems and to present a new tool to support the design of DRT systems. The proposed tool integrates a decision support system (DSS) specifically designed to assess the operating performance of different alternative system configurations to be implemented, allowing to choose the best solution found. The DSS is supported by a microscopic simulation model of the system operation, and even includes solution methods for different variants of the vehicle routing problem found in this type of transportation services, furthermore a framework to evaluate the solutions sustainability. To validate the developed DSS, we used a Portuguese case study. The test results allowed highlighting the DSS potentialities. Additionally, the solution sustainability assessment identified the hard financial self-sustaining of this type of systems, but also their advantages in both social and environmental impact that will probably be sufficient to justify its implementation.Fundação para a Ciência e Tecnologia SFRH/BD/60776/2009