[pt] Esta dissertação tem como objetivo contribuir para o gerenciamento de frotas de grande porte, buscando uma maior rapidez e eficiência na distribuição de veículos ao longo do tempo/espaço, visando maximizar o lucro total da empresa. Problemas de gerenciamento de frotas dinâmicas são normalmente formulados como uma rede dinâmica, mas há uma grande dificuldade ao se trabalhar com problemas desse tipo, especialmente quando se busca uma solução sobre um horizonte de planejamento longo. Visando contornar essa dificuldade, Powell & Carvalho (1998) desenvolveram uma nova abordagem para problemas desse tipo: a Logistics Queuing Network (LQN). A utilização do algoritmo LQN na prática (através de um software) permitiria uma tomada de decisão mais rápida e eficiente, sendo bastante útil, em especial para empresas de transportes. Assim, implementou-se o algoritmo LQN, através do desenvolvimento de um software para o gerenciamento de frotas de grande porte, por meio do qual podese constatar o potencial de aplicação desse algoritmo. / [en] The objective of this dissertation is to contribute to the large-scale fleet management, looking for a greater efficiency and speed in vehicle distribution over time and space, while maximizing total profit. Dynamic fleet management problems are normally formulated as a dynamic network, but it`s really difficult to work with problems of this class, especially when we look for a solution over a large planning horizon. In order to overcome this problem, Powell & Carvalho (1998) developed a new formulation for these problems: the Logistics Queuing Network. The use of its algorithm in real problems (using a software) would allow quickly and more efficient decisions in transports, being really useful especially for transports enterprises. For this reason, the algorithm LQN was implemented, through the development of a software for the large-scale fleet management, so we could verify the potential application of this algorithm.

The heavy duty vehicle industry has today no requirement to providea tire pressure monitoring system by law. This has created issues sur-rounding unknown tire pressure and thread depth during active service.There is also no standardization for these kind of systems which meansthat different manufacturers and third party solutions work after theirown principles and it can be hard to know what works for a given vehicletype. National Highway Traffic Safety Administration (NHTSA) put out a new study that determined that underinflated tires of 25% or less are 3 times more likely to be involved in a crash related to tire issues versus vehicles with properly inflated tires. The objective for this thesis is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. Drivec Bridge hardware interprets existing sensors from the vehicle. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues.The classifier will classify the vehicles tires condition. The classifier will be implemented in Drivecs cloud service and use data to classify  the tires condition. The resulting classifier of the project is a random forest implemented in Python. The result from the front axle with a dataset consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of90.54% (±0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. The project group has verified with high accuracy that a vehicle has been classified as bad and then been reclassified as good over a time span of 16 days. At the first day offboard measurements were performed and it showed that the tires of the front axle were underinflated. The classifier indicated that the vehicle had bad classifications until day 14. At this day an offboard measurement was performed and it was concluded that they were no longer underinflated and the classifier indicated this as well. To verify the result the workshop was contacted and verified that the vehicle had changed tires of the front axle at day 14. This has verified that the classifier is able to detect change and stay consistent in the results over a longer time period.

Aim of this master thesis is to describe the service of Full Service Leasing, as a modern form of financing and management of assets, primarily automobile fleet. Description of full service leasing is designed as a comprehensive and complete guide to support reader's position when deciding to finance and manage a fleet by this service. Whether the reader is an entrepreneur, CFO, fleet manager, new employee of leasing company, or anyone who is interested in this service, this master thesis will give him information that would otherwise be obtained only very fragmented. Chapters individualy present full service leasing from different perspectives and author's comments are linking them in a single unit.

Subject of this diploma thesis Processes and vehicle fleet optimization at the selected company is to make the processes more effective and to reduce costs associated with operation of vehicle fleet at selected enterprise. The average cost per kilometer, usage of journeys made by vehicles, strengths and weaknesses of an enterprise are observed within analyzes. Processes are evaluated overall through analyzes and after that, suggested suitable solutions are provided.

In this contemporary world, when the globalization is on the first place, is possible to satisfy one's needs immediately. Road haulage becomes in last few years a significant market's part, which is necessary to the right function in all branches in the Czech Republic. The aim of this thesis is to analyse level of principle operation's research's support in TASW solution for the society, which is directed at road transport. Simultaneously I project a study, which supports a solution for small shippers, who aren't able or willing to acquire an expensive software for haulage entrepreneurship. I characterize issues of transportation's company to achieve my aim and I think about the principle's uses and method of operation's research in process of haulage entrepreneurship. I analyse a situation of operation's research's support with TASW, which is oriented on haulage entrepreneurship. After that I describe a study, where I project a solution for small shippers, which takes in the consideration the components of operation's research in their processes and TASW and this contributes to make the decision of small shippers easier to buy the suitable software for haulage. This thesis is divided into few parts -- at the begging I theoretically follow up haulage entrepreneurship, its division and kind of current freight. I describe the price creation to make really obvious, which knowledge the shipper have to know. I explain particular disciplines of research's operation, which are connected to make more effective the workings of haulage entrepreneurship. I use these results of the theoretical parts to create my own model of haulage entrepreneurship. I analyse the market's system in the Czech market, which is fixed to support process of haulage entrepreneurship, which applies the principles of operation's research. In conclusion I compare the variants of using different types of IS for transport. The biggest benefit of my thesis I see in that also the laymen can understand it. It makes easier to choose a specialized software for haulage entrepreneurship and the process analyse binding on possibly company's analyse and on field operation's research, which enable more effective delivery.

This thesis is focused on the monitoring system in the fleet management. Frauds are increasingly caused due to company cars. The emphasis is put especially on costs saving resulting from the introduction of the monitoring and a more effective control. The work defines modern elements of control, their functions and impact on costs. It outlines pros and cons connected with the journey log. Practical examples are given as an illustration, including an alternative solution with a savings calculation.

Résumé indisponible. / The VIPAFLEET project aims at developing a framework to manage a fleet of IndividualPublic Autonomous Vehicles (VIPA). We consider a fleet of cars distributed at specifiedstations in an industrial area to supply internal transportation, where the cars can beused in different modes of circulation (tram mode, elevator mode, taxi mode). The goalis to develop and implement suitable algorithms for each mode in order to satisfy all therequests either under an economic point aspect or under a quality of service aspect, thisby varying the studied objective functions.We model the underlying online transportation system as a discrete event basedsystem and propose a corresponding fleet management framework, to handle modes,demands and commands. We consider three modes of circulation, tram, elevator andtaxi mode. We propose for each mode appropriate online algorithms and evaluate theirperformance, both in terms of competitive analysis and practical behavior by computationalresults. We treat in this work, the pickup and delivery problem related to theTram mode and the Elevator mode the pickup and delivery problem with time windowsrelated to the taxi mode by means of flows in time-expanded networks.

In recent years, advances in information technology have led to an increasing number of devices (or things) being connected to the internet; the resulting data can be used by applications to acquire new knowledge. The Internet of Things (IoT) (a network of computing devices that have the ability to interact with their environment without requiring user interaction) and big data (a field that deals with the exponentially increasing rate of data creation, which is a challenge for the cloud in its current state and for standard data analysis technologies) have become hot topics. With all this data being produced, new applications such as predictive maintenance are possible. One such application is monitoring a fleet of vehicles in real-time to predict their remaining useful life, which could help companies lower their fleet management costs by reducing their fleet's average vehicle downtime. Consensus self-organized models (COSMO) approach is an example of a predictive maintenance system for a fleet of public transport buses, which attempts to diagnose faulty buses that deviate from the rest of the bus fleet. The present work proposes a novel IoT-based architecture for predictive maintenance that consists of three primary nodes: namely, the vehicle node (VN), the server leader node (SLN), and the root node (RN). The VN represents the vehicle and performs lightweight data acquisition, data analytics, and data storage. The VN is connected to the fleet via its wireless internet connection. The SLN is responsible for managing a region of vehicles, and it performs more heavy-duty data storage, fleet-wide analytics, and networking. The RN is the central point of administration for the entire system. It controls the entire fleet and provides the application interface to the fleet system. A minimally viable prototype (MVP) of the proposed architecture was implemented and deployed to a garage of the Soci\'et\'e de Transport de l'Outaouais (STO), Gatineau, Canada. The VN in the MVP was implemented using a Raspberry Pi, which acquired sensor data from a STO hybrid bus by reading from a J1939 network, the SLN was implemented using a laptop, and the RN was deployed using meshcentral.com. The goal of the MVP was to perform predictive maintenance for the STO to help reduce their fleet management costs. The present work also proposes a fleet-wide unsupervised dynamic sensor selection algorithm, which attempts to improve the sensor selection performed by the COSMO approach. I named this algorithm the improved consensus self-organized models (ICOSMO) approach. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a STO hybrid bus, which was acquired using the MVP, was used to generate synthetic data to simulate vehicles, faults, and repairs. The deviation detection of the COSMO and ICOSMO approach was applied to the synthetic sensor data. The simulation results were used to compare the performance of the COSMO and ICOSMO approach. Results revealed that in general ICOSMO improved the accuracy of COSMO when COSMO was not performing optimally; that is, in the following situations: a) when the histogram distance chosen by COSMO was a poor choice, b) in an environment with relatively high sensor white noise, and c) when COSMO selected poor sensors. On average ICOSMO only rarely reduced the accuracy of COSMO, which is promising since it suggests deploying ICOSMO as a predictive maintenance system should perform just as well or better than COSMO . More experiments are required to better understand the performance of ICOSMO. The goal is to eventually deploy ICOSMO to the MVP.

In recent years, the paradigm of personal urban mobility has radically evolved as an increasing number of Mobility-on-Demand (MoD) systems continue to revolutionize urban transportation. Hailed as the future of sustainable transportation, with significant implications on urban planning, these systems typically utilize a fleet of shared vehicles such as bikes, electric scooters, cars, etc., and provide a centralized matching platform to deliver point-to-point mobility to passengers. In this dissertation, we study MoD systems along three operational directions – (1) modeling: developing analytical models that capture the rich stochasticity of passenger demand and its impact on the fleet distribution, (2) economics: devising strategies to maximize revenue, and (3) control: developing coordination mechanisms aimed at optimizing platform throughput. First, we focus on the metropolitan bike-sharing systems where platforms typically do not have access to real-time location data to ascertain the exact spatial distribution of their fleet. We formulate the problem of accurately predicting the fleet distribution as a Markov Chain monitoring problem on a graph representation of a city. Specifically, each monitor provides information on the exact number of bikes transitioning to a specific node or traversing a specific edge at a particular time. Under budget constraints on the number of such monitors, we design efficient algorithms to determine appropriate monitoring operations and demonstrate their efficacy over synthetic and real datasets. Second, we focus on the revenue maximization strategies for individual strategic driving partners on ride-hailing platforms. Under the key assumption that large-scale platform dynamics are agnostic to the actions of an individual strategic driver, we propose a series of dynamic programming-based algorithms to devise contingency plans that maximize the expected earnings of a driver. Using robust optimization techniques, we rigorously reason about and analyze the sensitivity of such strategies to perturbations in passenger demand distributions. Finally, we address the problem of large-scale fleet management. Recent approaches for the fleet management problem have leveraged model-free deep reinforcement learning (RL) based algorithms to tackle complex decision-making problems. However, such methods suffer from a lack of explainability and often fail to generalize well. We consider an explicit need-based coordination mechanism to propose a non-deep RL-based algorithm that augments tabular Q-learning with a combinatorial optimization problem. Empirically, a case study on the New York City taxi demand enables a rigorous assessment of the value, robustness, and generalizability of the proposed approaches.

A rapidly growing interest in augmented and virtual reality within industrial areas such as manufacturing, quality control, and fleet monitoring has been seen in the last couple of years. This technology shift could bring a new era to the industry sector in the near future. This study aims to evaluate if using virtual reality can be a more efficient way of monitoring lots of data than a traditional monitor based solution or not. In this study, a virtual reality application has been created in order to provides a virtual environment where operators can access and monitor their assets, which a proof-of-concept digital model represents. The digital model presents information about the components from a physical asset’s current state and status. This VR application was then evaluated in an A/B test against an existing monitor-based dashboard application. The A/B test was conducted with 10 participants performing 11 different tasks. The results show that VR technology could be a promising solution for operating and monitoring fleet unit assets, with an overall improvement in the efficiency of 17% for all of the participants.