This dissertation proposes a framework in support of biomass supply chain network design. This framework relies in the use of trucks for short distance biomass transportation, and relies in the use of rail for long-haul, and high-volume transportation of densified biomass. A hub and spoke network design model is proposed for the case when biomass is shipped by rail. These models are created and solved for the following problems: 1) designing a biomass supply chain to deliver densified biomass to a coal fired power plant for coiring and 2) designing biomass-to-biorefinery supply chain using rail for long-haul, and high-volume shipment of densified biomass under economic, environmental, and social criteria. The first problem is modeled as a Mixed-Integer Linear Programming (MILP). A Benders’ decomposition-based algorithm is developed to solve the MILP model because its large size makes it difficult to solve using CPLEX. The numerical analysis indicates that the total unit transportation cost from the farm to a coal plant is $36/ton. Numerical analysis also indicates that biomass cofiring is cost efficient compare to direct coal firing if the renewable energy production tax credit is applied and biomass is located within 75 miles from a coal plant. The second problem is also modeled as a MILP mode. This MILP identifies the number, capacity and location of biorefineries needed to make use of the biomass available in the region. A case study is created using data from a number of States in the Midwest USA. The numerical analysis show that 24.38%-26.12% of the target cellulosic biofuel set by the Energy Independence and Security Act of 2007 can be met at delivery cost $4.01 to $4.02 per gallon. The numerical analysis also reveals the tradeoffs that exist among the economics, environmental impact, and social objectives of using densified biomass for production of biofuel. Finally, this dissertation presents a detailed analysis of the rail transportation cost for products that have similar physical characteristics to densified biomass and biofuel. A numbers of regression equations are developed in order to evaluate and quantify the impact of important factors on the unit transportation cost.

The aim of this thesis is to evaluate fatigue management systems (FMSs) within the Australian transportation industry, and provide directions for future improvement. In doing so, it draws on some preliminary data from the rail industry, and a larger study that evaluates several components of a FMS that was implemented by the Australian Civil Aviation Safety Authority (CASA) for the general aviation sector in 2000.

This research presents a rail transportation cost analysis of bulk agricultural commodities (such as grain and wood chips) with similar characteristics as pre-processed biomass. This study analyzes the cost factors that affect rail pricing for shipments of bulk-commodities (such as grain) from the Midwest to various regions in the US using regression analysis theories. The rail cost equations developed from the regression analysis were used to compare the trade-offs that exist between truck, rail and barge transportation of pre-processed biomass.

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

This Master Thesis focuses on rail transport in Japan. It describes the evolution of Japanese railroads from the beginning until present day, when is Japan the leading country in personal rail transportation. Further, there are described major railway operators and high-speed railway network Shinkansen. Later it explains modern system Maglev, which is momentarily built in Japan. The analysis focuses on financial indicators and specific traits of Japanese railroads. In the end the author compares Shinkansen against other means of transport on two major routes of different length. He asks question how introduction of new forms of rail transportation can change the Japan.

Transit operators are increasingly interested in improving efficiency, reliability, and performance of commuter trains while reducing their operating costs. In this context, the application of optimal control theory to the problem of train control can help towards achieving some of these objectives. However, the traction and braking systems of commuter trains often exhibit significant time delays, making the control of commuter trains highly challenging. Previous literature on optimal train control ignores delays in actuation due to the inherent difficulty present in the optimal control, and in general, the control, of input-delay systems. In this thesis, optimal control of a commuter train is presented under two cases: (i) equal, and (ii) unequal time delays in the train traction and braking commands. The solution approach uses the economic model predictive control framework, which involves formulating and solving numerical optimization problems to achieve minimum mixed energy-time optimal control in discretized spatial and time domains. The optimization problems are re-solved repeatedly along the track for the remainder of the trip, using the latest sensor measurements. This would essentially establish a feedback mechanism in the control to improve robustness to modelling errors. A key feature of the proposed methods is that they are model-based controllers, they explicitly incorporate model information, including time delays, in controller synthesis hence avoiding performance degradation and potential instability. To address the issue of input-delays, the well-established predictor approach is used to compensate for input-delays. The case of equal traction-braking delays is treated in discretized spatial domain, which uses an already developed convex approximation to the optimization problem. The use of the convex approximation allows for robust and rapid computation of the optimal control solution. The non-equal traction-braking delays scenario is formulated in time domain, leading to a nonconvex optimization problem. An alternative formulation for minimum-time optimal control problems is presented for delay-free systems that simplifies the solution of minimum-time optimal control problems compared to conventional minimum-time optimal control formulations. This formulation along with the predictor approach is used to help solve the train optimal control problem in the case of non-equal traction-braking delays. The non-equal traction-braking delay controller is compared with the equal traction-braking delay controller by insertion of an artificial delay to make the shorter delays equal to the longer delay. Results of numerical simulations demonstrate the validity and effectiveness of the proposed controllers. / Thesis / Master of Applied Science (MASc)

Yes / The pressing necessity to renew infrastructure assets in developed railway systems leads to an increased number of activities to be scheduled annually. Scheduling of renewal activities for a railway network is a critical task because these activities often require a significant amount of time and create a capacity conflict in operation scheduling. This paper discusses economic and technological aspects, opportunities, and constraints in the renewals of multiple rail infrastructure components at several locations in a railway network. We addressed and modeled a challenging situation in which there were interrelationships between different track lines, and thus, possession of a track line could affect the other track lines and prevent renewal works on them. A mathematical formulation for the railway infrastructure renewal scheduling problem in the network context was presented to minimize the total renewal and unavailability costs. A method based on a triple-prioritization rule and an optimal sharing of renewal times allocated for different types of rail infrastructure components in a possession is proposed to solve the problem. The method was applied to a real case of a regional railway network in Northern Netherlands and it was shown that up to 13% of total costs can be saved compared with the current scheduling practice.

Cette thèse développe des algorithmes pour des problèmes de transport ferroviaire et est réalisée en partenariat avec l'entreprise Eurotunnel qui exploite le tunnel sous la Manche. Ce partenariat s'est établi sous la forme d'une chaire avec l'École des Ponts où cette thèse a été menée. Nous développons trois sujets dans cette thèse: le premier est un problème opérationnel rencontré par Eurotunnel, les deux autres sont plus prospectifs et théoriques, et sont inspirés des problèmes de transport ferroviaire d'Eurotunnel.Le processus de création de grilles horaires pour le transport ferroviaire se découpe en plusieurs phases (estimation de la demande, détermination du réseau, planification des départs, affectation des trains et du personnel). Nous nous intéressons dans une première partie à la phase de planification des départs des trains sur un intervalle temporel, appliquée au cas spécifique d'Eurotunnel. L'objectif est de calculer les horaires des départs des trains depuis chacune des deux stations (Coquelles en France et Folkestone en Angleterre) en respectant des contraintes d'exploitation (sécurité, chargement, ...) et des accords commerciaux signés avec leurs partenaires (Eurostar, ...). De plus, la prise en compte des retards dès la planification des départs est primordiale pour limiter la propagation des perturbations de train en train sur le réseau. Nous avons développé des algorithmes de planification pour Eurotunnel tenant compte des contraintes du réseau et de la probabilité de retard pour chaque train. Ces algorithmes utilisent des outils standard de la Recherche Opérationnelle pour modéliser et résoudre ces problèmes d'optimisation.La tarification des billets est un enjeu majeur pour les entreprises de transport. Pour les compagnies aériennes, de nombreux algorithmes ont été étudiés pour définir le prix optimal des billets pour différentes classes de passagers. Nous appliquons dans une deuxième partie des méthodes standard de tarification (modèles de choix discrets) afin d'optimiser de manière globale les prix et les horaires des départs pour des entreprises de transport ferroviaire. Des outils classiques de l'optimisation stochastique, des modèles de choix discrets et des heuristiques sont utilisés dans nos algorithmes pour donner les meilleures solutions possibles en un temps de calcul limité.Nous nous intéressons dans une dernière partie à une classe de problèmes de transport, inspirés de ceux rencontrés par Eurotunnel, en donnant des algorithmes efficaces de résolution exacte ou approchée. Ces algorithmes permettent de donner une borne supérieure de la complexité temporelle de ces problèmes. La classe de problèmes étudiés consiste en la planification des départs de navettes sur une ligne fixe, pour transporter d'une station A vers une station B des usagers arrivant de manière continue. Les navettes sont éventuellement autorisées à faire de multiples rotations pour transporter plusieurs vagues d'usagers. L'objectif est de limiter le temps d'attente des passagers avant le départ de leur navette. Des combinaisons originales de l'optimisation convexe et de la théorie des graphes (problèmes de plus court chemin) sont utilisées dans nos algorithmes / This thesis develops algorithms for rail transportation problems, conducted in relationship with the company Eurotunnel which operates the tunnel under the Channel. This partnership is a scientific chair with the École des Ponts et Chaussées, where this thesis was realized. We study three topics throughout the thesis: the first one is an operational problem faced by Eurotunnel, whereas the two other ones are prospective and theoretical problems inspired by their process.The planning process for rail transportation can be divided into several phases (demand estimation, line planning, scheduling of the departure times, rolling stock and crew planning). In a first part, we focus on the scheduling phase on a time interval, applied to the specific case of Eurotunnel. The objective is to compute the departure times of the trains for each of the two stations (Calais in France and Folkestone in England), satisfying operation constraints (security, loading, ...) and commercial agreements with their partners (Eurostar, ...). Moreover, taking into account the delays in the scheduling phase is essential to limit the propagation of the disturbances from train to train in the network. We develop scheduling algorithms for Eurotunnel taking into account the operation and commercial constraints, and the random distributions of the delays for each train. These algorithms use standard tools of Operations Research to model and solve these optimization problems.Pricing is a main issue for transportation companies. Many algorithms have been proposed to help airline companies to define optimized prices of the plane tickets for different classes of passengers. In a second part, we apply some standard pricing frameworks (discrete choice models) in order to optimize in a global way the prices and the departure times of the trains for rail transportation companies. Standard tools of stochastic optimization, discrete choice models, and some heuristics are used in our algorithms to compute the best possible solutions in a limited computation time.We focus in a last part on a class of transportation problems, inspired form Eurotunnel. We give efficient algorithms to solve exactly or to approximate the optimal solutions of these problems. These algorithms give an upper bound of the time complexity of this class of problems. The problems studied consist in scheduling the departure times of shuttles on a fixed trip, to transport passengers, arriving continuously at an initial station, to a given destination. The shuttles are potentially allowed to perform several rotations to transport several groups of passengers. The objective is to minimize the waiting time of the passengers before the depart of their shuttle. Original combinations of convex optimization and graph theory (shortest path problems) are used in our algorithms

The major purpose of this thesis is to offer a detailed look at the development of two models used to assist in the detailed study of Australian two lane two way highways with particular reference to heavy vehicles. The first model governs the acceleration behaviour of vehicles on upgrades and downgrades. The second model controls overtaking manoeuvres on two lane two way highways where movement into the lane of oncoming traffic is required. Both models are implemented through a suite of transport simulation modelling software called Paramics.