Smooth and controlled recovery planning of disruptions in rapid transit networks

This paper studies the disruption management problem of rapid transit rail networks. We consider an integrated model for the recovery of the timetable and the rolling stock schedules. We propose a new approach to deal with large-scale disruptions: we limit the number of simultaneous schedule changes as much as possible, and we control the length of the recovery period, in addition to the traditional objective criteria such as service quality and operational costs. Our new criteria express two goals: the recovery schedules can easily be implemented in practice, and the operations quickly return to the originally planned schedules after the recovery period. We report our computational tests on realistic problem instances of the Spanish rail operator RENFE and demonstrate the potential of this approach by solving different variants of the proposed model

COMPETITIVE ADVANTAGE: HOW TAP PORTUGAL KEEPS THE EDGE CONNECTING EUROPE AND LATIN AMERICA

The present work, as its main purpose, investigates of whether operational, demand, and home country factors influence an airline’s competitive position relative to its peers. Operational efficiency through cost management and hub strategies is a widely known subject in the airline business, and pressure from the deregulation of several air transport markets around the world and subsequent fierce competition on this service industry has increased the need for differentiation based on the aspect that more and more weighs on customer choice: costs and pricing. This investigation researches TAP Portugal against European competitors Iberia and Lufthansa on its service offerings to Latin America, making conclusions on whether the Portuguese airline has indeed a competitive advantage supported by its geography on serving that market

Aplicação de Modelos Semi-Integrados de Programação de Voos e Alocação de Aeronaves Utilizando Algoritmo Genético

TCC (graduação) - Universidade Federal de Santa Catarina, Campus Joinville, Engenharia de Transportes e Logística.A Programação de Voos consiste em sequenciar um conjunto de voos a partir de pares Origem-Destino com janelas de operações definidas, escolhidos na etapa anterior de Desenvolvimento de Rotas, enquanto Alocação de Aeronaves consiste em escolher o modelo de aeronave que irá atender cada programação de voos. Tais etapas são as que mais impactam nos custos de uma companhia aérea e, tratar cada uma de forma isolada pode levar a resultados insatisfatórios, seja em alocar um modelo de aeronave incompatível com a demanda da programação de voos, seja não gerar o sequenciamento ideal para cada modelo de aeronave. No entanto, buscar aplicar as duas etapas de forma integrada gera modelos da classe NP-hard, aumentando a complexidade computacional e exigindo a utilização de meta-heurísticas. Neste contexto, este trabalho concentrou-se em analisar dois modelos matemáticos relacionados à Programação de Voos e Alocação de Aeronaves, sendo que o primeiro busca minimizar a perda de receitas e o segundo busca minimizar o Momento de Transporte, dado pelo produto entre o número de passageiros não transportados e o tempo de viagem, e, para a busca de solução dos referidos problemas, foi desenvolvido um Algoritmo Genético. Os dados utilizados no estudo são de uma empresa aérea regional. Ao final deste trabalho, são apresentados os resultados operacionais, econômicos, e de robustez das programações de voos geradas por cada modelo

Modelo matemático como soporte para la planificación del transporte masivo de pasajeros aplicando una estrategia de cambio de resolución

En esta tesis se formula un modelo matemático de optimización para resolver de manera integrada las etapas de diseño de itinerarios y asignación de flota en un sistema de transporte aéreo de pasajeros utilizando una estrategia de cambio de resolución para disminuir el tamaño del problema resultante, en términos de la cantidad de variables de decisión y ecuaciones, así como del tiempo y de la cantidad de iteraciones requeridas para resolverlo. Para reducir el tamaño del modelo de optimización resultante se implementa una estrategia de clusterización de datos utilizando algoritmos de Aprendizaje de Máquina e Inteligencia Artificial. Estos algoritmos permiten agrupar datos en clústers de manera no trivial, de manera que los elementos pertenecientes a cada clúster son homogéneos entre sí, y los clústers contienen elementos heterogéneos entre ellos. Así, un conjunto original de datos pasa a ser reemplazado por los centroides de los clústers encontrados. Se desarrolla un caso de aplicación en el que, usando el modelo de optimización y la estrategia de cambio de resolución propuesta, se resuelven las dos etapas de la planeación mencionadas. Se plantea el modelo con y sin clusterización de datos y se concluye que la estrategia de clusterización, además de disminuir drásticamente el tiempo de resolución del modelo, mejora la calidad de la solución encontrada, ya que se obtiene una combinación de vuelos incluidos en el itinerario operada con un costo menor que el óptimo encontrado sin aplicar la clusterización de datos y con mejor conectividad entre ellos.Abstract: In this thesis, a mathematical optimization model to solve the integrated problem of itinerary design and fleet assignment in a passenger air transportation system is formulated using a change-of-scale strategy to reduce the size of the resulting problem, in terms of the number of decision variables and constraints, as well as the time and number of iterations required to solve it. To reduce the size of the resulting model, a clustering strategy is implemented using Machine Learning and Artificial Intelligence algorithms. Such algorithms allow to group data in clusters, in a non-trivial way, so that the elements belonging to one cluster are similar among them, and the clusters contain dissimilar elements. This way, an original data set is replaced by the centroids of the clusters found. An application case is developed to solve the mentioned integrated problem using the proposed optimization model and change-of-scale strategy. The model is solved with and without data clustering. The data clustering strategy, besides drastically reducing the resolution time of the model, improves the quality of the solution found, due to a higher flexibility to find a combination of flights included in the final itinerary with higher connectivity between them and operated with a lower cost than the optimal found without the data clustering.Maestrí

Conjoint design of railway lines and frequency setting under semi-congested scenarios

This thesis develops mathematical programming models which integrate network design (ND) and line frequency setting (LFS) phases. These appear in transport planning studies that extend an existing urban public transportation system (UPTS) and are suitable for underground and rapid transit systems. The ND phase extends the working UPTS, taking as inputs the locations of candidate stretches and stations on the new lines, as well as construction costs which cannot exceed the available infrastructure budget. Regarding the LFS phase, frequencies and vehicles are assigned to functioning and newly built lines, providing that they do not exceed resource capacities and the time horizon. The developed models take into account the type of service patterns that may operate on the lines of the transport system. They include local services, where vehicles halt at every node in the line, and express services, in which vehicles halt at only a subset of nodes in the line. A passenger assignment model allows solving, simultaneously, the ND and LFS phases under a system optimum point of view. The combined model has two variants: one which deals with inelastic demand and another which faces elasticities in demand. The latter originates from changes in the modal choice proportions of travelers and may result from modifications in the public transport system. The former does not take into account competition among several modes of transportation and it is formulated as a mixed-integer linear programming problem. In contrast, the latter allows passengers to travel via two modes of transportation: public transport and private car. It is formulated as a mixed-integer linear bi-level programming problem (MILBP) with discrete variables only in the upper level. In both models, a complementary network is used to model transfers among lines and to reach the passenger¿s origin and/or destination nodes when the constructed UPTS does not cover them. The model with inelastic demand is initially solved by means of the commercial solver CPLEX under three different mathematical formulations for the ND phase. The first two are exact approaches based on extensions of Traveling Salesman Problem formulations for dynamic and static treatment of the line¿s subtours, whereas the last one is an approximation inspired by constrained k-shortest path algorithms. In order to deal with large-sized networks, a quasi-exact solution framework is employed. It consists of three solving blocks: the corridor generation algorithm (CGA), the line splitting algorithm (LSA), and a specialized Benders decomposition (SBD). The LSA and CGA are heuristic techniques that allow skipping some of the non-polynomial properties. They are related to the number of lines under construction and the number of feasible corridors that can be generated. As for the SBD, it is an exact method that splits the original mathematical programming problem into a series of resolutions, composed of two mathematical problems which are easier to solve. Regarding the elastic demand variant, it is solved under the same framework as the specialized Benders decomposition adaptation for solving MILBP, which results from this variant formulation. The inelastic demand variant is applied to two test cases based on underground network models for the cities of Seville and Santiago de Chile. Origin destination trip matrices and other parameters required by the models have been set to likely values using maps and published studies. The purpose of these networks is to test the models and algorithms on realistic scenarios, as well as to show their potentialities. Reported results show that the quasi-exact approach is comparable to approximate techniques in terms of performance. Regarding the elastic demand variant, the model is more complex and can be applied only to smaller networks. Finally, some further lines of research for both modeling and algorithmic issues are discussed