A Decision Support model for planning Intercity Railways is presented in this research. The main aim of the model is to generate inputs for the logit model existing in the Virginia Tech Transportation Systems Analysis Model (TSAM). The inputs required by the TSAM logit model are travel time, travel cost and schedule delay. Travel times and travel costs for different rail technologies are calculated using a rail network and actual or proposed rail schedules. The concept of relational databases is used in the development of the network topology. Further, an event graph approach is used for analysis of the generated network. Shortest travel times and their corresponding travel costs between origin-destination pairs are found using Floyd's algorithm. Complete itineraries including transfers (if involved) are intrinsically held in the precedence matrix generated after running the algorithm. A standard mapping technique is used to obtain the actual routes. The algorithms developed, have been implemented in MATLAB. Schedules from the North American Passenger rail system AMTRAK are used to generate the sample network for this study. The model developed allows the user to evaluate what-if scenarios for various route frequencies and rail technologies such as Accelerail, High Speed Rail and Maglev. The user also has the option of modifying route information. Comparison of travel time values for the mentioned technology types in different corridors revealed that frequency of service has a greater impact on the total travel time in shorter distance corridors, whereas technology/line-haul speed has a greater influence on the total travel time in the longer distance corridors. This tool could be useful to make preliminary assessments of future rail systems. The network topology generated by the algorithm can further be used for network flow assignment, especially time-dependent assignment if used with dynamic graph algorithms. / Master of Science

Cette thèse présente une méthodologie d’ordonnancement cyclique robuste appliquée à la gestion des conteneurs dans les ports maritimes de taille moyenne. Ces derniers sont sujet constamment à des variations des conditions des terminaux, la visibilité réduite sur des évènements futurs ne permet pas de proposer une planification précise des tâches à accomplir. L’ordonnancement cyclique robuste peut jouer un rôle primordial. Il permettra non seulement de proposer un ordonnancement prédictif pour le transport des conteneurs, mais aussi, il proposera également une planification robuste permettant d’éliminer les perturbations éventuelles en temps réel. Dans ce travail nous utilisons les Véhicules Intelligents Automatisés (AIV) pour transporter les conteneurs et nous modélisons les procédures de transit de ces derniers par des graphes d’évènements P-temporels fortement connexes (PTSCEG). Avant l’arrivée d’un porte conteneur au port, un plan (planning) de transport des conteneurs est proposé en un temps court par la programmation linéaire mixte (MIP). Des algorithmes polynomiaux de calcul de robustesse permettent de calculer sur les différents nœuds du système les marges de robustesse. Une fois le navire à quai, l’ordonnancement cyclique robuste est appliqué. Lorsqu’une perturbation est observée (localisée) dans le système, une comparaison avec la marge de robustesse connue est effectuée. Si cette perturbation est incluse dans la marge de robustesse, l’algorithme robuste est utilisé pour éliminer ces perturbations en quelques cycles. Dans le cas où la perturbation est trop importante, la méthode MIP est utilisée pour calculer un nouvel ordonnancement cyclique en un temps réduit / This PhD thesis is dedicated to propose a robust cyclic scheduling methodology applied to container management of medium sized seaport which faces ever changing terminal conditions and the limited predictability of future events and their timing. The robust cyclic scheduling can be seen not just a predictable scheduling to compute a container transportation schedule, but also a reactive scheduling to eliminate the disturbances in real time. In this work, the automated intelligent vehicles (AIV) are used to transport the containers, and the P-time strongly connected event graph (PTSCEG) is used as a graphical tool to model the container transit procedures. Before the arrival of the container vessel, a cyclic container transit schedule can be given by the mixed integer programming (MIP) method in short time. The robustness margins on the nodes of the system can be computed by robustness algorithms in polynomial computing time. After the stevedoring begins, this robust cyclic schedule is used. When a disturbance is observed in system, it should be compared with the known robustness margin. If the disturbance belongs to the robustness margin, the robustness algorithm is used to eliminate the disturbance in a few cycle times. If not, the MIP method is used to compute a new cyclic schedule in short time