The purpose of this study was to develop and test an optimization model that will provide a deployment plan of emission reduction technologies to reduce emissions from non-road equipment. The focus of the study was on the counties of Texas that have nonattainment (NA) and near-nonattainment (NNA) status. The objective of this research was to develop methodologies that will help to deploy emission reduction technologies for non-road equipment of TxDOT to reduce emissions in a cost effective and optimal manner. Three technologies were considered for deployment in this research, (1) hydrogen enrichment (HE), (2) selective catalytic reduction (SCR) and (3) fuel additive (FA). Combinations of technologies were also considered in the study, i.e. HE with FA, and SCR with FA. Two approaches were investigated in this research. The first approach was "Method 1" in which all the technologies, i.e. FA, HE and SCR were deployed in the NA counties at the first stage. In the second stage the same technologies were deployed in the NNA counties with the remaining budget, if any. The second approach was called "Method 2" in which all the technologies, i.e. FA, HE and SCR were deployed in the NA counties along with deploying only FA in the NNA counties at the first stage. Then with the remaining budget, SCR and HE were deployed in the NNA counties in the second stage. In each of these methods, 2 options were considered, i.e. maximizing NOx reduction with and without fuel economy consideration in the objective function. Thus, the four options investigated each having different mixes of emission reduction technologies include Case 1A: Method 1 with fuel economy consideration; Case 1B: Method 1 without fuel economy consideration; Case 2A: Method 2 with fuel economy consideration; and Case 2B: Method 2 without fuel economy consideration and were programmed with Visual C++ and ILOG CPLEX. These four options were tested for budget amounts ranging from $500 to $1,183,000 and the results obtained show that for a given budget one option representing a mix of technologies often performed better than others. This is conceivable because for a given budget the optimization model selects an affordable option considering the cost of technologies involved while at the same time maximum emission reduction, with and without fuel economy consideration, is achieved. Thus the alternative options described in this study will assist the decision makers to decide about the deployment preference of technologies. For a given budget, the decision maker can obtain the results for total NOx reduction, combined diesel economy and total combined benefit using the four models mentioned above. Based on their requirements and priorities, they can select the desired model and subsequently obtain the required deployment plan for deploying the emission reduction technologies in the NA and NNA counties.

O problema de alocação de aeronaves a voos, ou tail assignment problem (TAP), consiste em determinar qual aeronave realizará cada voo da malha de uma empresa aérea, visando a minimizar o custo total da operação e respeitando diversas restrições de conectividade de voos, permanência de aeronaves no solo, serviços obrigatórios de manutenção, limitações técnicas e desempenho de aeronaves, conexões de passageiros e tripulantes e famílias com diversos modelos de aeronaves. Este trabalho apresenta um modelo matemático exato e um método heurístico para a solução do TAP considerando todas as restrições citadas, o que não ocorre com os modelos encontrados na literatura. Os modelos desenvolvidos, baseados em programação linear inteira e na meta-heurística Busca Tabu, foram aplicados a problemas reais, extraídos da malha de uma empresa aérea brasileira, operadora de 35 aeronaves e cerca de 210 voos diários. Os resultados obtidos são compatíveis com a operação da empresa e apresentam ganhos em relação ao método de alocação de aeronaves utilizado na operação diária. Os tempos de processamento para solução pelo método exato são excessivamente longos, indicando que o método heurístico é mais adequado para a utilização em empresas aéreas, com resultados adequados obtidos em tempos de processamento satisfatórios. / The problem known as Aircraft Assignment or Tail Assignment Problem (TAP) is the problem of assigning flights to each aircraft of an airline\'s fleet, aiming at minimizing the total operating cost while complying with several constraints, such as network connectivity, aircraft time on ground, mandatory maintenance services, aircraft technical restrictions, passengers and crew connections, aircraft performance and aircraft families with more than one type. This work presents a deterministic mathematical model and a heuristic method to solve the TAP considering all constraints listed above, what does not happen with the models found in the literature. The proposed methods, based on mathematical integer programming and on the Tabu Search metaheuristic, were applied to problems obtained from the network of a Brazilian airline, operating 35 aircraft and around 210 daily flights. The results show the models are suitable to solve the problem and savings are observed when compared to the current assignment method. The long processing times intrinsic to the deterministic method show the heuristic method is more suitable for use in airlines, with suitable results obtained at acceptable computational times.

O problema de alocação de aeronaves a voos, ou tail assignment problem (TAP), consiste em determinar qual aeronave realizará cada voo da malha de uma empresa aérea, visando a minimizar o custo total da operação e respeitando diversas restrições de conectividade de voos, permanência de aeronaves no solo, serviços obrigatórios de manutenção, limitações técnicas e desempenho de aeronaves, conexões de passageiros e tripulantes e famílias com diversos modelos de aeronaves. Este trabalho apresenta um modelo matemático exato e um método heurístico para a solução do TAP considerando todas as restrições citadas, o que não ocorre com os modelos encontrados na literatura. Os modelos desenvolvidos, baseados em programação linear inteira e na meta-heurística Busca Tabu, foram aplicados a problemas reais, extraídos da malha de uma empresa aérea brasileira, operadora de 35 aeronaves e cerca de 210 voos diários. Os resultados obtidos são compatíveis com a operação da empresa e apresentam ganhos em relação ao método de alocação de aeronaves utilizado na operação diária. Os tempos de processamento para solução pelo método exato são excessivamente longos, indicando que o método heurístico é mais adequado para a utilização em empresas aéreas, com resultados adequados obtidos em tempos de processamento satisfatórios. / The problem known as Aircraft Assignment or Tail Assignment Problem (TAP) is the problem of assigning flights to each aircraft of an airline\'s fleet, aiming at minimizing the total operating cost while complying with several constraints, such as network connectivity, aircraft time on ground, mandatory maintenance services, aircraft technical restrictions, passengers and crew connections, aircraft performance and aircraft families with more than one type. This work presents a deterministic mathematical model and a heuristic method to solve the TAP considering all constraints listed above, what does not happen with the models found in the literature. The proposed methods, based on mathematical integer programming and on the Tabu Search metaheuristic, were applied to problems obtained from the network of a Brazilian airline, operating 35 aircraft and around 210 daily flights. The results show the models are suitable to solve the problem and savings are observed when compared to the current assignment method. The long processing times intrinsic to the deterministic method show the heuristic method is more suitable for use in airlines, with suitable results obtained at acceptable computational times.

No description available.

No description available.