The role of statistical methodology in simulation

statistical methods;simulation;operations research

Forecasting model development and application in the aviation industry

Forecasting models have been applied to many industries as a decision-making tool for over 100 years. Their application in the aviation industry benefits a wide variety of stakeholders such as airliners and airport authorities, who use past data to predict demand and passenger choices so that they can better define fares, manage their fleet and make decisions on the airport layout and future expansions, among others. The main objective of this dissertation is the development of a forecasting model capable of predicting the number of flight movements at Lisbon Airport. The model was based on an autoregressive model, which uses past data in order to forecast future figures. Weekly data regarding the flight movements at Lisbon Airport was the sample for this study, which was processed through RStudio programming software. Once the Autoregressive Moving Average (ARIMA) models were defined, the forecasting data was created and further tested for accuracy using extant data. The impact of COVID-19 had to be considered in this situation, leading to the breakdown of the original time-series into three different samples. The forecasting models were subsequently created through each of these models. The results were expressed through the three different models, and since two of them have extant data, meaning an existing sample to compare the predicted data, it was possible to determine the accuracy level. However, these models cannot be applied immediately since the impact of COVID-19 is still present and flights have not resumed normality. Once this normality resumes, the models can be applied.Modelos preditivos têm sido aplicados a variados setores como ferramenta de tomada de decisão há mais de 100 anos. A sua aplicação na indústria aeronáutica beneficia uma ampla variedade de interessados, como companhias aéreas e autoridades aeroportuárias que utilizam dados para prever a procura, definir preços, gerir frotas e tomar decisões relativas ao layout do aeroporto, expansões futuras, entre outros. O principal objetivo desta dissertação é o desenvolvimento de um modelo de previsão capaz de prever o número de movimentos de voos no Aeroporto de Lisboa. O modelo foi baseado num modelo autorregressivo, que utiliza dados passados para prever valores futuros. O Aeroporto de Lisboa foi o objeto escolhido para esta dissertação. Dados semanais relativos aos movimentos aéreos no Aeroporto de Lisboa consistiram na amostra para este estudo, os quais foram processados através do software de programação RStudio. Assim que os modelos Autoregressive Moving Average (ARIMA) foram definidos, os dados de previsão foram criados e testados quanto à precisão usando os dados existentes. O impacto do COVID-19 teve que ser considerado nesta situação, levando à divisão da série temporal original em três amostras diferentes. Os modelos de previsão foram posteriormente criados através de cada um desses modelos. Os resultados foram expressos através dos três modelos, e como dois deles possuem dados existentes para comparação com dados previstos, foi possível determinar o nível de precisão. No entanto, os modelos não podem ser aplicados imediatamente, uma vez que o impacto do COVID-19 ainda está presente e os voos não voltaram à normalidade. Uma vez resumida essa normalidade, os modelos podem ser aplicados

OPTIMAL HEDGING STRATEGIES FOR THE U.S. CATTLE FEEDER

Multiproduct optimal hedging for simulated cattle feeding is compared to alternative hedging strategies using weekly price data for 1983-95. Out-of-sample means and variances of hedged feeding margins using estimated hedge ratios for four commodities suggest that there is no consistent domination pattern among the alternative strategies, leaving the hedging decision up to the agent's degree of risk aversion. However, all hedging strategies significantly reduce the feeding margin's means and variances compared to no hedging, with variance reduction always exceeding 50%. Hedging results appear quite sensitive to the data set and its size.cattle feeding, hedge ratios, hedging strategies, multiproduct hedging, optimal hedging, Marketing,

Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates.

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM?s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

A robust multi-objective statistical improvement approach to electric power portfolio selection

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Duncan, Scott; Committee Member: Ender, Tommer; Committee Member: German, Brian; Committee Member: Paredis, Chri