Trade liberalization and resultantly globalization has led to the relative free flow of goods, services, capital and people. The transport system in South Africa must be highly reliable and rapid to contribute to economic development. The focus of this research study is on the Port/Rail Interface in the Port of Durban which critically assesses the transfer of goods particularly from rail transport to and from vessels within the port precinct. Some of the major constraints identified in the study related to poor infrastructure, operations and levels of services; lack of maintenance, availability of rolling stock, locomotives and cargo stacking space. Transnet's role is pivotal in providing efficient port and rail operations and is currently upgrading infrastructure, operations and capacity of the port and rail services. One of the main objectives of the study is to present recommendations to Transnet management that arise out of the shortcomings identified during the course of the research study. / Transport Economics / M. Comm.

Trade liberalization and resultantly globalization has led to the relative free flow of goods, services, capital and people. The transport system in South Africa must be highly reliable and rapid to contribute to economic development. The focus of this research study is on the Port/Rail Interface in the Port of Durban which critically assesses the transfer of goods particularly from rail transport to and from vessels within the port precinct. Some of the major constraints identified in the study related to poor infrastructure, operations and levels of services; lack of maintenance, availability of rolling stock, locomotives and cargo stacking space. Transnet's role is pivotal in providing efficient port and rail operations and is currently upgrading infrastructure, operations and capacity of the port and rail services. One of the main objectives of the study is to present recommendations to Transnet management that arise out of the shortcomings identified during the course of the research study. / Transport Economics / M. Comm.

The work in this thesis is separated into two parts. The first part deals with the load planning and sequencing problem for double-stack intermodal railcars, an operational problem found at many rail container terminals. In this problem, containers must be assigned to a platform on which the container will be loaded, and the loading order must be determined. These decisions are made with the objective of minimizing the costs associated with handling the containers, as well as minimizing the cost of containers left behind. The deterministic version of the problem can be cast as a shortest path problem on an ordered graph. This problem is challenging to solve because of the large size of the graph. We propose a two-stage heuristic based on the Iterative Deepening A* algorithm to compute solutions to the load planning and sequencing problem within a five-minute time budget. Next, we also illustrate how a Deep Q-learning algorithm can be used to heuristically solve the same problem.The second part of this thesis considers sequential models in deep learning. A recent strategy to circumvent the exploding and vanishing gradient problem in recurrent neural networks (RNNs) is to enforce recurrent weight matrices to be orthogonal or unitary. While this ensures stable dynamics during training, it comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a parameterization of RNNs, based on the Schur decomposition, that mitigates the exploding and vanishing gradient problem, while allowing for non-orthogonal recurrent weight matrices in the model. / Le travail de cette thèse est divisé en deux parties. La première partie traite du problème de planification et de séquencement des chargements de conteneurs sur des wagons, un problème opérationnel rencontré dans de nombreux terminaux ferroviaires intermodaux. Dans ce problème, les conteneurs doivent être affectés à une plate-forme sur laquelle un ou deux conteneurs seront chargés et l'ordre de chargement doit être déterminé. Ces décisions sont prises dans le but de minimiser les coûts associés à la manutention des conteneurs, ainsi que de minimiser le coût des conteneurs non chargés. La version déterministe du problème peut être formulé comme un problème de plus court chemin sur un graphe ordonné. Ce problème est difficile à résoudre en raison de la grande taille du graphe. Nous proposons une heuristique en deux étapes basée sur l'algorithme Iterative Deepening A* pour calculer des solutions au problème de planification et de séquencement de la charge dans un budget de cinq minutes. Ensuite, nous illustrons également comment un algorithme d'apprentissage Deep Q peut être utilisé pour résoudre heuristiquement le même problème. La deuxième partie de cette thèse examine les modèles séquentiels en apprentissage profond. Une stratégie récente pour contourner le problème de gradient qui explose et disparaît dans les réseaux de neurones récurrents (RNN) consiste à imposer des matrices de poids récurrentes orthogonales ou unitaires. Bien que cela assure une dynamique stable pendant l'entraînement, cela se fait au prix d'une expressivité réduite en raison de la variété limitée des transformations orthogonales. Nous proposons une paramétrisation des RNN, basée sur la décomposition de Schur, qui atténue les problèmes de gradient, tout en permettant des matrices de poids récurrentes non orthogonales dans le modèle.