1,198 research outputs found

    A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification

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    The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs

    System-of-Systems Considerations in the Notional Development of a Metropolitan Aerial Transportation System

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    There are substantial future challenges related to sustaining and improving efficient, cost-effective, and environmentally friendly transportation options for urban regions. Over the past several decades there has been a worldwide trend towards increasing urbanization of society. Accompanying this urbanization are increasing surface transportation infrastructure costs and, despite public infrastructure investments, increasing surface transportation "gridlock." In addition to this global urbanization trend, there has been a substantial increase in concern regarding energy sustainability, fossil fuel emissions, and the potential implications of global climate change. A recently completed study investigated the feasibility of an aviation solution for future urban transportation (refs. 1, 2). Such an aerial transportation system could ideally address some of the above noted concerns related to urbanization, transportation gridlock, and fossil fuel emissions (ref. 3). A metro/regional aerial transportation system could also provide enhanced transportation flexibility to accommodate extraordinary events such as surface (rail/road) transportation network disruptions and emergency/disaster relief responses

    A decision support system for the management of smart mobility services

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    Master Dissertation (Master Degree in Engineering and Management of Information Systems)Nos dias que correm, a mobilidade assume especial importância no quotidiano das áreas metropolitanas em crescimento no país. . Com o notório crescimento das cidades, torna-se necessária e urgente uma transformação dos costumes e formas de mobilidade dentro das áreas urbanas, alterando as realidades aparentes que hoje conhecemos. Inseridos numa sociedade cada vez mais consciencializada e alerta para as questões ambientais, é essencial transportar esta mentalidade renovada para a resolução das problemáticas citadinas. Assim, o conceito de “Cidade Verde” levanta uma série de questões que exigem uma resposta eficaz para o bem-estar dos seus habitantes. Por entre as várias soluções apresentadas para estas patologias, uma das mais promissoras é, sem dúvida, o sistema de mobilidade partilhada. Pela sua dimensão, é pertinente expor o caso prático da cidade de Barcelona, em Espanha, explorando o seu sistema de partilha de scooters, um meio que adquire especial importância como meio de transporte urbano. Como qualquer sistema em constante aprimoramento, procura-se uma solução para a problemática da variação de procura, que apresenta oscilações constantes, tanto a nível temporal como geográfico, resultando na falta de veículos em algumas áreas e excesso noutras. Assim sendo, o rebalanceamento do sistema torna-se crucial para uma possível maximização na utilização de veículos, satisfazendo a procura e potenciando um aumento da sua utilização. No correr desta dissertação, foram estudados e utilizados vários métodos de otimização moderna (metaheurísticas) para a procura de soluções (sub)ótimas para o(s) percurso(s) a percorrer pelo(s) veículo(s) que executam a redistribuição das scooter/bicicletas pelas diversas áreas abrangidas pelo sistema de partilha. Deste modo, foi desenvolvido um sistema de apoio à decisão para satisfazer estas necessidades, garantindo ao utilizador toda a informação relevante para um trabalho mais eficiente e preciso.Nowadays, mobility is especially important in the daily life of the country growing metropolitan areas. With the increasing influx of people and development of these large cities, the reality of mobility that we know becomes increasingly unsustainable. Along with mobility, the environmental concerns are one of the main topics of discussion worldwide and the population is starting to act and change the way they live to find a more “green” and sustainable way of doing it. Several proposals have been put forward, trying to mitigate this issue and, one of the most promising is, undoubtedly, shared mobility systems. In this case study will be addressed the Barcelona scooter sharing system, characterized by its great size and importance as a mean of urban transport. One of the problems presented by these sharing services is that demand varies widely, both temporal and geographical. Thus, there are several cases where there is a lack of vehicles in some areas and an excess in others. The rebalancing of the system is crucial to maximize vehicle utilization and meet customer demand. In this thesis, several modern optimization methods (metaheuristics) were used to search for (sub)optimal solutions for the redistribution route(s). A decision support system was developed to meet this end, giving the end user relevant information for a more efficient and precise work

    Drone Fleet Deployment Strategy for Large Scale Agriculture and Forestry Surveying

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    International audienceAgriculture drones offer clear advantages over other monitoring methods including satellite imaging, manned scouting, and manned aircraft. However, for large scale areas, such as large forestry and agriculture mapping problems, the single drone is hard to accomplish its mission of mapping in a relatively short time period of 30 to 45 minutes. In addition, in large forestry mapping, camera, communication, and payload settings may further reduce the maximum endurance of drones in the air. With a single drone, the total required mission time to cover all the area is prolonged, not only producing a high cost for a drone service provider but also having more uncertainty. While with multiple drones, or a fleet of drones, it is possible to identify a globally optimized solution to reduce the total required mission time. In this paper, we mainly discuss the strategy of drone fleet deployment for large scale area surveying. Three key parts are analyzed, including a fleet of drones, cooperative coverage path planning, communication and data processing. The associated state-of-the-art solutions are listed and reviewed. In addition, in this paper, the key operational constraints for large scale agriculture and forestry surveying are analyzed. It should be pointed out that, from a comprehensive point of view, a drone fleet deployment for large scale surveying could attract more attention from the commercial drone industry

    Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

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    To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions
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