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[pt] A volatilidade inerente ao mercado de fretes marítimos e as incertezas relacionadas à demanda de transportes prevista contribuem para a complexidade do problema de dimensionamento da frota. Este trabalho aborda o problema da renovação da frota marítima de uma empresa brasileira do setor de óleo e gás, para o transporte, em viagens de cabotagem e longo curso, de derivados de petróleo. Para tal, é apresentado um modelo estocástico de programação inteira-mista de dois estágios para capaz de gerar indicações de contratos de afretamento a serem realizados considerando incertezas nos níveis de mercado de fretes e na previsão de volume movimentado. O modelo é capaz de fornecer composições de frota capazes de atender as especificações do problema, contudo, para os casos analisados, a avaliação das soluções obtidas ao se considerar a incerteza mostrou potencial de ganho pouco significativo em comparação com uma modelagem similar considerando valores esperados dos parâmetros. Este trabalho evidencia uma situação em que é útil a avaliação das soluções Wait-and-See (WS) e Expected Value of Expected Solution (EEV), menos demandantes computacionalmente, para calcular o potencial ganho da solução do modelo estocástico. / [en] The inherent volatility in the maritime freight market and the uncertainties related to the expected transport demand contribute to the complexity of the fleet size and mix problem. This work addresses the problem of the maritime fleet renewal of a Brazilian oil and gas company, for the transportation, in cabotage and international voyages, of oil products. To this end, we present a two-stage stochastic mixed-integer programming model capable of giving recommendations of which chartering contracts to be performed, considering uncertainties in freight market levels and in the forecasted volume movement. The model is able to provide fleet compositions capable of meeting the problem specifications, however, in the evaluated cases, little gain potential was observed by comparing the stochastic solutions to solutions considering expected parameter values. This work highlights a situation in which the evaluation of the computationally less demanding Waitand-See (WS) and Expected Value of Expected Solution (EEV) solutions is useful to calculate the potential gain of the stochastic model solution.

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.

The following thesis deals with optimization of freight transport planning. The goal is to minimize expenses connected to transportation, which emerge from travelled distance. The expenses can be heavily reduced, if the routes are correctly planned, especially when there is a large number of customers to be served. This thesis focuses on solving the problem by using the evolutional algorithms, that are optimization methods based on principles of evolution. Thesis concentrates on Heterogeneous Fixed Fleet Vehicle Routing Problem. Thesis introduces multiple evolutional algorithms and their results are compared. The best algorithm, evolutional strategy with local neighbourhood search, achieves similar, for certain tasks even better results, than other existing evolutional algorithms, created to solve given problem.

In today’s world of volatile fuel prices and climate concerns, there is little study on the relation between vehicle ownership patterns and attitudes toward potential policies and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s household-fleet evolution. Results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are vehicle purchase price, type/class, and fuel economy (with 30%, 21% and 19% of respondents placing these in their top three). Most (56%) respondents also indicated that they would seriously consider purchasing a Plug-In Hybrid Electric Vehicle (PHEV) if it were to cost $6,000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings. 25-year simulations suggest that 19% of Austin’s vehicle fleet could be comprised of Hybrid Electric Vehicles (HEVs) and PHEVs under adoption of a feebate policy (along with PHEV availability in Year 1 of the simulation, and current gas prices throughout). Under all scenarios vehicle usage levels (in total vehicle miles traveled [VMT]) are predicted to increase overall, along with average vehicle ownership levels (per household, and per capita); and a feebate policy is predicted to raise total regional VMT slightly (just 4.43 percent, by simulation year 25), relative to the trend scenario, while reducing CO2 emissions only slightly (by 3.8 percent, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 17% and CO2 emissions by 22% (relative to trend). Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. And HEVs, PHEVs and Smart Cars are estimated to represent a major share of the fleet’s VMT (25%) by year 25 under the feebate scenario. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet), yet feebate-policy receipts exceed rebates in each simulation year. A 15% reduction in the usage levels of SUVs, CUVs and minivans is observed in the $5/gallon scenario (relative to trend). Mean use levels per vehicle of HEVs and PHEVs are simulated to have a variation of 753 and 495 across scenarios. In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have even more significant effects on energy dependence and greenhouse gas emissions. / text

Transportation fleet emissions have a dominant role in air quality because of their significant contribution to ozone precursor and greenhouse gas emissions. Regulatory policies have emphasized improvements in vehicle fuel economy, alternative fuel use, and engine and vehicle technologies as approaches for obtaining transportation systems that support sustainable development. This study examined the air quality impacts of the partial electrification of the transportation fleet and the use of biofuels for the Austin Metropolitan Statistical Area under a 2030 vision of regional population growth and urban development using the Comprehensive Air Quality Model with extensions (CAMx). Different strategies were considered including the use of Plug-in Hybrid Electric Vehicles (PHEVs) with nighttime charging using excess capacity from electricity generation units and the replacement of conventional petroleum fuels with different percentages of the biofuels E85 and B100 along or in combination. Comparisons between a 2030 regional vision of growth assuming a continuation of current development trends (denoted as Envision Central Texas A or ECT A) in the Austin MSA and the electrification and biofuels scenarios were evaluated using different metrics, including changes in daily maximum 1-hour and 8-hour ozone concentrations, total area, time integrated area and total daily population exposure exceeding different 1-hour ozone concentration thresholds. Changes in ozone precursor emissions and predicted carbon monoxide and aldehyde concentrations were also determined for each scenario. Maximum changes in hourly ozone concentration from the use of PHEVs ranged from -8.5 to 2.2 ppb relative to ECT A. Replacement of petroleum based fuels with E85 had a lesser effect than PHEVs on maximum daily ozone concentrations. The maximum reduction due to replacement of 100% of gasoline fuel in light and heavy duty gasoline vehicles by E85 ranged from -2.1 to 2.8 ppb. The magnitude of the effect was sensitive to the biofuel penetration level. Unlike E85, B100 negatively impacted hourly ozone concentrations relative to the 2030 ECT A case. As the replacement level of petroleum-diesel fuel with B100 in diesel vehicles increased, hourly ozone concentrations increased as well. However, changes due to the penetration of B100 were relatively smaller than those due to E85 since the gasoline fraction of the fleet is larger than the diesel fraction. Because of the reductions in NOx emissions associated with E85, the results for the biofuels combination scenario were similar to those for the E85 scenario. Also, the results showed that as the threshold ozone concentration increased, so too did the percentage reductions in total daily population exposure for the PHEV, E85, and biofuel combination scenarios relative to ECT A. The greatest reductions in population exposure under higher threshold ozone concentrations were achieved with the E85 100% and 17% PHEV with EGU controls scenarios, while the B100 scenarios resulted in greater population exposure under higher threshold ozone concentrations. / text

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

A presente pesquisa aborda o problema de dimensionamento de frota de navios rebocadores do tipo AHTS, que são utilizados essencialmente nas tarefas de operações de apoio à exploração e produção de petróleo offshore (em alto mar). Essas atividades se caracterizam pela requisição simultânea de múltiplos navios de classes diferentes, e possuem parâmetros como: compatibilidade de classes de navios com as tarefas, duração em dias, local de execução e instante desejado de atendimento. Para representar este problema foi desenvolvido um modelo de simulação com parâmetros estocásticos, cuja programação é orientada para minimização dos custos totais da operação, que englobam custos fixos, custos de penalidade por atraso no atendimento das tarefas, e penalidade por falta de cumprimento de tarefas. A abordagem de solução do modelo é a busca exaustiva onde são comparados cenários de simulação de eventos discretos. Adicionalmente, foram comparadas 2 modos de escolhas de tarefas na fila de tarefas, o primeiro é o modo FIFO (First In First Out), o segundo modo é a priorização de tarefas com maior custo de penalidade associado para o dimensionamento de frota. / This research addresses a fleet sizing problem of anchor and handling and tug supply vessels (AHTS), which support the exploration and production of oil at the sea. The support activities are characterized by simultaneous request of multiple vessels of one or more classes. Other characteristics of the research problem are:: the compatibility between vessels and tasks, task duration (in days), a place of execution the task and a desired instant to be attended. A simulation model with stochastic parameters was developed to represent this problem, aiming to minimize the total operational cost that includes fixed costs,penalty costs if tasks are delayed and penalty costs with not completed tasks. The strategy to solve this problem was the exhausted search through discrete-event simulation. Aditionally, 2 methods of approach for the queue were analyzed: the first one is the FIFO (First In First Out) and the second one is the priority according the highest penalty cost to size the fleet.