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.

[pt] A presente dissertação aborda a análise do contingente atual e futuro da frota terceirizada utilizada pela empresa para a distribuição de óleo lubrificante a granel no Brasil. O objetivo central é propor uma melhor utilização dos recursos logísticos, visando ganhos operacionais e financeiros. Inicialmente, é destacada a importância dos óleos lubrificantes para veículos e equipamentos motorizados e a relevância do mercado de lubrificantes no contexto econômico brasileiro. Uma análise prévia fornecida pela companhia revelou que aproximadamente 55 por cento do valor pago em contrato, valor referente a disponibilidade da frota, às transportadoras não vem sendo revertido em ganhos para a empresa. Evidenciando um gap na eficiência da operação. O objetivo dessa dissertação, portanto, é identificar as demandas e projetar cenários futuros para três propostas principais: reduzir a quantidade de veículos disponíveis, expandir a operação aumentando os volumes vendidos ou aplicar as duas propostas simultaneamente, reduzindo o contingente e aumentando os volumes vendidos. A proposta de expansão do volume vendido no contingente atual indicou um ganho superior à redução da frota, tendo sido considerados diversos custos e receitas, sendo, portanto, a opção recomendada. Este trabalho também contribui para a literatura acadêmica e para a prática empresarial ao fornecer uma visão abrangente sobre a gestão de frota terceirizada no contexto brasileiro de distribuição de óleos lubrificantes. Utilizando metodologias quantitativas, foram fornecidas recomendações práticas para otimizar operações logísticas, melhorando a competitividade e sustentabilidade da empresa. Ao final desse estudo foram observados possíveis estudos futuros, com o objetivo de aprofundar mais sobre o tema. / [en] This dissertation addresses the analysis of the current and future contingent of the outsourced fleet used by the company for the distribution of bulk lubricating oil in Brazil. The main objective is to propose a better use of logistics resources, aiming at operational and financial gains. Initially, the importance of lubricating oils for vehicles and motorized equipment and the relevance of the lubricants market in the Brazilian economic context are highlighted. A previous analysis provided by the company revealed that approximately 55 percent of the amount paid in the contract, the amount referring to the availability of the fleet, to the transporters has not been reverted into gains for the company, highlighting a gap in the efficiency of the program. The objective of this dissertation, therefore, is to identify the demands and project future scenarios for three main proposals: reduce the number of vehicles available, expand the program by increasing the volumes sold or apply both proposals simultaneously, reducing the contingent and increasing the volumes sold. The proposal to expand the volume sold in the current contingent indicated a gain greater than the reduction of the fleet, having considered several costs and revenues, and is, therefore, the recommended option. This work also contributes to the academic literature and business practice by providing a comprehensive overview of outsourced fleet management in the Brazilian context of lubricating oil distribution. Using quantitative methodologies, practical recommendations were provided to optimize logistics operations, improving the company s competitiveness and sustainability. At the end of this study, possible future studies were observed, with the aim of further deepening the topic.

Pour relever les défis grandissants auxquels est confronté le système de mobilité en termes de durabilité, les autorités publiques françaises ont entrepris de soutenir l'innovation, notamment dans le domaine automobile. Les entreprises occupent une position particulière au sein du système de mobilité : leurs décisions influencent les comportements de mobilité bien au-delà des seuls déplacements professionnels. Chaque année en France, 4 véhicules légers sur 10 parmi les voitures particulières et véhicules utilitaires neufs mis sur le marché sont acquis par des entreprises. Pourtant, la mobilité professionnelle en général, et les flottes automobiles d'entreprise en particulier, demeurent des zones d'ombre de la connaissance du système de mobilité. L'objet de notre travail et sa contribution principale est de démontrer que, compte tenu de leurs effets sur l'ensemble du système de mobilité d'une part, de leur sensibilité aux politiques publiques d'autre part, les flottes automobiles d'entreprise constituent un objet pertinent tant pour la recherche que pour l'action publique. Notre investigation s'appuie sur des méthodes multiples : outre une synthèse de sources bibliographiques variées (journaux professionnels, archives légales, etc.), nous proposons des recoupements originaux entre, d'une part, des données quantitatives sur la composition et l'usage des flottes automobiles d'entreprise issues d'enquêtes de grande envergure et, d'autre part, les résultats qualitatifs d'une enquête exploratoire menée auprès de gestionnaires de flotte en région parisienne. Nous développons un ensemble de définitions et de cadres analytiques pour étudier les flottes automobiles d'entreprise, et notamment une typologie de véhicules basée sur les différents niveaux de « droits » accordés à l'utilisateur du véhicule d'entreprise. Nous montrons que les flottes automobiles d'entreprise totalisent 15% de l'ensemble des véhicules légers en France, 25% de leur kilométrage et 25% à 30% de leurs émissions de CO2.Par ailleurs, nous révélons le rôle essentiel que peuvent jouer les flottes automobiles d'entreprise pour amorcer des changements dans le parc automobile français. Nous montrons que les usages quotidiens des véhicules d'entreprise sont très divers, et dans certains cas compatibles avec les véhicules électriques. Enfin, nous mettons en évidence les effets tangibles des politiques fiscales sur la dynamique de diffusion des innovations au sein des flottes automobiles d'entreprise. Nous examinons les implications de ces résultats en termes de politiques publiques, en soulignant le besoin d'une plus grande intégration entre politiques industrielles, politiques de transport et politiques fiscales. Nous mettons en particulier en évidence le besoin d'une coordination accrue entre les politiques publiques menées à différentes échelles et d'un phasage approprié de ces politiques. Dans le contexte actuel, une plus grande transparence semble requise quant à la pérennité des mesures d' « amorçage » (par exemple, le bonus à l'achat) et au volontarisme des mesures de « soutien » (par exemple, les zones à basses émissions) qui pourraient être adoptées à moyen ou long terme / The mobility system in France faces increasing sustainability challenges. In response, French public authorities have endeavoured to foster innovation in the mobility system, with a particular focus on the automotive subsystem, where the challenges are most acute. Corporations have a special position in the mobility system: their decisions influence mobility behaviours well beyond corporate mobility patterns alone. Every year in France, 4 out of 10 new light-duty vehicles (including passenger cars and light commercial vehicles) are sold to corporations. Yet, corporate mobility in general, and corporate car fleets in particular, are still blind spots in the collective understanding of the mobility system. The main contribution of our work is to demonstrate that, given their effects on the larger mobility system, and given their sensitivity to public policies, corporate car fleets are a relevant object for research and a relevant matter for public policy discussion. Our research is a multi-method investigation, collecting information from a wide range of sources, including professional journals and legal archives, and cross-checking quantitative results on the composition and use patterns of corporate car fleets from large mobility surveys in France against qualitative insights gained from an exploratory survey of fleet managers in the Paris region. We develop a set of definitions and analytical frameworks for investigating corporate car fleets, including a typology of vehicles based on the various levels of ‘rights' granted to the employee over the vehicle. We show that corporate car fleets could account for 15% of the total light-duty vehicle fleet in France, 25% of its total mileage and 25% to 30% of its CO2 emissions. We also reveal the instrumental role that corporate car fleets can play in setting new trends for France's global vehicle stock. We highlight that the daily patterns of use of corporate vehicles are highly diverse, and partly compatible with electric vehicles. Finally, we show that tax policies have significant effects on the dynamics of the spread of innovations in corporate car fleets. We discuss the implications of these results for policy-making and stress the need for further integration between industrial policies, transport policies, and tax policies. We further emphasise the need for greater coordination between the various levels of government, and for adequate phasing of public policies. At present, more transparency is needed about how long current ‘initiating' policies (e.g. purchase bonus) will last, and how strong the ‘supporting' policies (e.g. low-emission zones) will be in the medium- to long-term

This research focuses on fleet management in freight transportation systems. Effective management requires effective planning and control decisions. Plans are often generated using estimates of how the system will evolve in the future; during execution, control decisions need to be made to account for differences between actual realizations and estimates. The benefits of minimum cost plans can be negated by performing costly adjustments during the operational phase. A planning approach that permits effective control during execution is proposed in this dissertation. This approach is inspired by recent work in robust optimization, and is applied to (i) dynamic asset management and (ii) vehicle routing problems. In practice, the fleet management planning is usually decomposed in two parts; the problem of repositioning empty, and the problem of allocating units to customer demands. An alternative integrated dynamic model for asset management problems is proposed. A computational study provides evidence that operating costs and fleet sizes may be significantly reduced with the integrated approach. However, results also illustrate that not considering inherent demand uncertainty generates fragile plans with potential costly control decisions. A planning approach for the empty repositioning problem is proposed that incorporates demand and supply uncertainty using interval around nominal forecasted parameters. The intervals define the uncertainty space for which buffers need to be built into the plan in order to make it a robust plan. Computational evidence suggests that this approach is tractable. The traditional approach to address the Vehicle Routing Problem with Stochastic Demands (VRPSD) is through cost expectation minimization. Although this approach is useful for building routes with low expected cost, it does not directly consider the maximum potential cost that a vehicle might incur when traversing the tour. Our approach aims at minimizing the maximum cost. Computational experiments show that our robust optimization approach generates solutions with expected costs that compare favorably to those obtained with the traditional approach, but also that perform better in worst-case scenarios. We also show how the techniques developed for this problem can be used to address the VRPSD with duration constraints.