This thesis explores how revenue management (RM) principles would integrate into a parking system, and how advanced reservation-making, coupled with dynamic pricing (based on booking limits) could be used to maximize parking revenue. Detailed here is a comprehensive RM strategy for the parking industry, and an integer programming formulation that maximizes parking revenue over a system of garages is presented. Furthermore, an intelligent parking reservation model is developed that uses an artificial neural network procedure for online reservation decision-making. Next, the work evaluates whether the implementation of a parking RM system in a dense urban parking district (and thus avoiding "trial-and-error" behaviors exhibited by drivers) mitigates urban congestion levels. In order to test this hypothesis, a parallel modeling structure was developed that uses a real-time decision-making model that either accepts or rejects requests for parking via a back-propagation neural network. Coupled with the real-time decision-making model is a micro-simulation model structure used to evaluate the policy's effects on network performance. It is clear from the results that the rate at which parkers renege is a primary determinant of the value of the implementation of RM. All other things being equal, the RM model in which the majority of parkers is directed to their precise parking spot via the most direct route is much more robust to the random elements within the network that can instigate extreme congestion. The thesis then moves from micro-evaluation to macro-evaluation by measuring the performance of the urban parking system from the perspective of the set of relevant stakeholders using the hyperbolic DEA model within the context of the matrix DEA construct. The stakeholder models, including that of the provider, the user, and the community, have defined inputs/outputs to the hyperbolic DEA model, which allows for the inclusion of undesirable outputs such as network delay and incidence of extreme congestion. Another key contribution of this work is that of identifying design issues for current and future dense urban parking districts. Clearly, reneging rate and the tenacity of perspective parkers is a key consideration in cases where RM policy is not implemented. / Ph. D.

本論文針對隨機數位網路提出一套可行的計算機制，以提供網路管理者進行資源分配與壅塞管理的分析工具。我們研究兩種利潤最佳化模型，探討在預算控制下的頻寬分配方式。因為資源有限，網路管理者無法隨時提供足夠頻寬以滿足隨機的網路需求，而量測網路連結成功與否的阻塞機率(Blocking Probability)為評估此風險之一種指標。我們利用頻寬分配、網路需求量和虛擬端對端路徑的數量等變數，推導阻塞機率函數，並證明阻塞機率的單調性(Monotonicity)和凸性(Convexity)等數學性質。在不失一般性之假設下，我們驗證阻塞機率是(1)隨頻寬增加而變小；(2)在特定的頻寬分配區間內呈凸性；(3)隨網路需求量增加而變大；(4)隨虛擬路徑的數量增加而變小。 本研究探討頻寬分配與阻塞機率之關係，藉由推導單調性和凸性等性質，提供此兩種利潤模型解的最適條件與求解演算法。同時，我們引用經濟學的彈性概念，提出三種模型參數對阻塞機率變化量的彈性定義，並分別進行頻寬分配、網路需求量和虛擬路徑數量對邊際利潤函數的敏感度分析。當網路上的虛擬路徑數量非常大時，阻塞機率的計算將變得複雜難解，因此我們利用高負荷極限理論(Heavy-Traffic Limit Theorem)提供阻塞機率的估計式，並分析其漸近行為(Asymptotic Behavior)。本論文的主要貢獻是分析頻寬分配與阻塞機率之間的關係及其數學性質。網路管理者可應用本研究提出的分析工具，在總預算限制下規劃寬頻網路的資源分配，並根據阻塞機率進行網路參數的調控。 / This thesis considers the problem of bandwidth allocation on communication networks with multiple traffic classes, where bandwidth is determined under the budget constraint. Due to the limited budget, there exists a risk that the network service providers can not assert a 100% guaranteed availability for the stochastic traffic demand at all times. We derive the blocking probabilities of connections as a function of bandwidth, traffic demand and the available number of virtual end-to-end paths for all service classes. Under general assumptions, we prove that the blocking probability is directionally (i) decreasing in bandwidth, (ii) convex in bandwidth for specific regions, (iii) increasing in traffic demand, and (iv) decreasing in the number of virtual paths. We also demonstrate the monotone and convex relations among those model parameters and the expected path occupancy. As the number of virtual paths is huge, we derive a heavy-traffic queueing model, and provide a diffusion approximation and its asymptotic analysis for the blocking probability, where the traffic intensity increases to one from below. Taking the blocking probability into account, two revenue management schemes are introduced to allocate bandwidth under budget control. The revenue/profit functions are studied in this thesis through the monotonicity and convexity of the blocking probability and expected path occupancy. Optimality conditions are derived to obtain an optimal bandwidth allocation for two revenue management schemes, and a solution algorithm is developed to allocate limited budget among competing traffic classes. In addition, we present three elasticities of the blocking probability to study the effect of changing model parameters on the average revenue in analysis of economic models. The sensitivity analysis and economic elasticity notions are proposed to investigate the marginal revenue for a given traffic class by changing bandwidth, traffic demand and the number of virtual paths, respectively. The main contribution of the present work is to prove the relationship between the blocking probability and allocated bandwidth under the budget constraint. Those results are also verified with numerical examples interpreting the blocking probability, utilization level, average revenue, etc. The relationship between blocking probability and bandwidth allocation can be applied in the design and provision of broadband communication networks by optimally choosing model parameters under budget control for sharing bandwidth in terms of blocking/congestion costs.

Le transport fluvial de conteneurs constitue une activité économique importante qui suscite un intérêt grandissant de la part de scientifiques. Considéré comme durable et économique, le transport par barge a été identifié comme étant une alternative compétitive pour le transport de marchandises, en complément des modes traditionnels de transport, routier et ferroviaire. Néanmoins, les travaux de recherche en rapport avec la planification et le management du transport par barge, en particulier dans le contexte du transport intermodal, sont encore peu abondants. Le but de cette thèse est d’apporter une contribution dans ce domaine, par la proposition de modèles et de méthodes de planification et gestion avancées, dans le cadre d’un système d’aide à la décision pour le transport de conteneurs par barge développé pour accompagner les opérateurs de transport. La méthodologie proposée fait appel à des concepts et principes de gestion du revenu, des ressources et des services de transport pour la conception de plans de services réguliers avec horaires, au niveau tactique. Les opérateurs de transport peuvent ainsi offrir des plans de transport avec des services plus flexibles pour leurs clients, tout en assurant un meilleur niveau de fiabilité. Plus de demandes de transport pourront ainsi être satisfaites, avec globalement une plus grande satisfaction des chargeurs. Une originalité importante proposée par notre approche est l’utilisation de principes et techniques de gestion du revenu (segmentation du marché, classes tarifaires...) aussi bien au niveau opérationnel de la modélisation qu’au niveau tactique. Les problèmes d’optimisation sont formalisés sous forme de modèles de programmation linéaire mixte en nombres entiers (PLNE), implémentés et testés sous différentes configurations de réseaux de transport et différents scénarios de demandes, et ce pour chaque niveau de décision. Au niveau tactique, une nouvelle approche de résolution, combinant la recherche adaptative à voisinage large (ALNS) et la recherche taboue, est proposée pour résoudre des problèmes PLNE de grande taille. Une plateforme de simulation, qui intègre les niveaux tactique et opérationnel de prise de décision, est proposée pour la validation du système d’aide à la décision sous différentes configurations : différentes topologies du réseau physique, différents paramètres pour la gestion du revenu, différents degrés de précision caractérisant les prévisions de demande. Pour l’analyse des résultats numériques ainsi obtenus, plusieurs types d’indicateurs de performance sont proposés et utilisés. / Barge transportation is an important research topic that started to draw increasing scientific attention in the recent decade. Considered as sustainable, environment-friendly and economical, barge transportation has been identified as a competitive alternative for freight transportation, complementing the traditional road and rail modes. However, contributions related to barge transportation, especially in the context of intermodal transportation, are still scarce. The objective of this thesis is to contribute to fill this gap by proposing a reactive decision support system for freight intermodal barge transportation from the perspective of the carriers. The proposed system incorporates resource and revenue management concepts and principles to build the optimal set of scheduled services plans at the tactical level. Carriers may thus benefit from transportation plans offering increased flexibility and reliability. They could thus serve more demands and better satisfy customers. One novelty of the approach is the application of revenue management considerations (e.g., market segmentation and price differentiation) at both operational and tactical planning levels. The optimization problems are mathematically formalized and mixed integer linear programming (MILP) models are proposed, implemented and tested against various network settings and demand scenarios, for each decision level. At the tactical level, a new solution approach, combining adaptive large neighborhood search (ALNS) and Tabu search is designed to solve large scale MILP problems. An integrated simulation framework, including the tactical and the operational levels jointly, is proposed to validate the decision support system in different settings, in terms of physical network topology, revenue management parameters and accuracy degree of demand forecasts. To analyze the numerical results corresponding to the solutions of the optimization problems, several categories of performance indicators are proposed and used.