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The trucking industry is vitally important to the economy, providing an essential service by transporting goods between businesses and consumers. The less-than-truckload (LTL) industry is an important segment, serving businesses that ship quantities between 150 lbs and 10,000 lbs. Large LTL carriers use thousands of drivers to move loads between terminals in their network, and each driver may be used for multiple dispatches between rest periods. Driver wages are a major component of transportation costs. Consequently, cost-effective driver management is of crucial importance for the profitability of LTL carriers. This thesis investigates a variety of issues related to driver management. In this thesis, we describe a dynamic driver scheduling scheme developed for a large U.S. LTL carrier. Dynamic driver scheduling is challenging because drivers must abide by a complex set of rules, including government and union regulations, and trucking moves are not pre-scheduled. The technology developed combines greedy search with enumeration of time-feasible driver duties, and is capable of generating cost-effective schedules covering 15,000 20,000 loads in minutes. One of the key tactical questions faced by an LTL carrier is how many drivers to locate at each terminal. Unionized carriers have bid drivers that can only move loads between their domicile and a designated region. The developed allocation technology determines the number of drivers to allocate to each terminal as well as the designated region for bid drivers. Computational experiments based on real-life dispatch data demonstrate the effectiveness of our domiciling methodology, and show that union rules may result in substantially larger driver fleets, in some cases up to 50% larger. Finally, we investigate a fundamental question related to driver management in order to obtain some fundamental insights: determining the minimum number of drivers required to cover a set of loaded moves. The problem is shown to be polynomially solvable without any restrictions on driver schedules. For variants with restrictions, several easily computable lower bounds are derived, integer programming formulations are presented, and fast heuristics are designed and analyzed. A computational study provides insights into the quality of the lower bounds and heuristic solutions.

Le routage multi-chemins est une technique qui permet l'équilibrage de la charge en multiplexant les flux vers une destination sur plusieurs chemins. Nous proposons une approche de routage multi-chemins qui, peut être schématisée en deux étapes : l'étape de choix des chemins candidats et l'étape de distribution du trafic sur un sous-ensemble de ces chemins. <br />Dans ce travail, nous avons commencé par effectuer une étude comparative d'un point scalabilité et stabilité, de plusieurs algorithmes de routage multi-chemins, basés sur MPLS. Cela nous a permis de retenir WDP pour la sélection des chemins candidats et LDM pour la distribution des demandes de trafic reçues par un routeur entrant du réseau d'un FAI. Nous proposons dans ce travail PER, un algorithme qui est une amélioration de l'algorithme de distribution de LDM. Ces différents algorithmes nous ont permis de réaliser plusieurs algorithmes « hybrides » dont LBWDP (Load Balancing over Widest Disjoint Paths) qui par simulation a été prouvé comme étant un algorithme plus performant que des modèles comme LDM ou MATE. <br />Pour une meilleure garantie de QdS nous avons cherché à intégrer la différentiation de service (DiffServ) avec notre technique d'ingénierie de trafic (DiffServ-aware MPLS Traffic Engineering : DS-TE). Nous proposons PEMS (PEriodic Multi-Step algorithm for DS-TE network) comme un modèle de DS-TE pour différencier la qualité du service selon la classe du trafic. Dans ce cadre, nous proposons un nouvel algorithme de sélection de chemins candidats en fonctions des critères préférentiels de chaque classe de trafic. L'utilisation de PER permet ensuite de distribuer en fonction de critères dynamiques les demandes reçues sur les meilleurs chemins de chaque classe. Par simulation à l'aide de ns-2, nous avons montré que PEMS répartie moins bien la charge que LBWDP mais que les classes EF et AF ont une qualité de service meilleure que dans le cas de LBWDP.

This thesis develops quantitative methods which incorporate transportation modeling for tactical and operational home health logistics planning problems. We define home health nurse routing and scheduling (HHNRS) problems, which are dynamic periodic routing and scheduling problems with fixed appointment times, where a set of patients must be visited by a home health nurse according to a prescribed weekly frequency for a prescribed number of consecutive weeks during a planning horizon, and each patient visit must be assigned an appointment time belonging to an allowable menu of equally-spaced times. Patient requests are revealed incrementally, and appointment time selections must be made without knowledge of future requests. First, a static problem variant is studied to understand the impact of fixed appointment times on routing and scheduling decisions, independent of other complicating factors in the HHNRS problem. The costs of offering fixed appointment times are quantified, and purely distance-based heuristics are shown to have potential limitations for appointment time problems unless proposed arc cost transformations are used. Building on this result, a new rolling horizon capacity-based heuristic is developed for HHNRS problems. The heuristic considers interactions between travel times, service times, and the fixed appointment time menu when inserting appointments for currently revealed patient requests into partial nurse schedules. The heuristic is shown to outperform a distance-based heuristic on metrics which emphasize meeting as much patient demand as possible. The home health nurse districting (HHND) problem is a tactical planning problem which influences HHNRS problem solution quality. A set of geographic zones must be partitioned into districts to be served by home health nurses, such that workload is balanced across districts and nurse travel is minimized. A set partitioning model for HHND is formulated and a column generation heuristic is developed which integrates ideas from optimization and local search. Methods for estimating district travel and workload are developed and implemented within the heuristic, which outperforms local search on test instances.

A load plan specifies how freight is routed through a linehaul terminal network operated by a less-than-truckload (LTL) carrier. Determining the design of the load plan is critical to effective operations of such carriers. This dissertation makes contributions in modeling and algorithm design for three problems in LTL load plan design: (1) Refined execution cost estimation. Existing load plan design models use approximations that ignore important facts such as the nonlinearity of transportation costs with respect to the number of trailers, and empty travel beyond what is required for trailer balance that results from driver rules. We develop models that more accurately capture key operations of LTL carriers and produce accurate operational execution costs estimates; (2) Dynamic load planning. Load plans are traditionally revised infrequently by LTL carriers due to the difficulty of solving the associated optimization problem. Technological advances have now enabled carriers to consider daily load plan updates. We develop technologies that efficiently and effectively adjust a nominal load plan for a given day based on the actual freight to be served by the carrier. We present an integer programming based local search procedure, and a greedy randomized adaptive search heuristic; and (3) Stochastic load plan design. Load plan design models commonly represent origin-destination freight volumes using average demands, which do not describe freight volume fluctuations. We investigate load plan design models that explicitly utilize information on freight volume uncertainty and design load plans that most cost-effectively deal with varying freight volumes and lead to the lowest expected cost. We present a Sample Average Approximation approach and a variant of the method for solving the stochastic integer programming formulations.

In this thesis, we study two stochastic vehicle routing problems. In the first part, we investigate a routing problem where the distributor wants to construct a set of delivery routes each day to serve a set of geographically dispersed customers, but wants to send the same driver to the same set of customers as much as possible due to business requirements. The stochastic nature of customer demands and the existence of hard delivery time windows make this fixed routes problem much harder. We introduce a new recourse policy based on limited vehicle sharing and develop heuristic approaches for constructing fixed routes respecting the new policy for large real-life instances. Among the key contributions is the introduction of sampling-based techniques to handle the feasibility issues arising from hard delivery windows. An extensive computational study based on real-life data demonstrates the efficacy of the proposed fixed routing system and route construction techniques. In the second part, we investigate the new policy in an abstract setting to understand its properties. We characterize the optimal traditional fixed routes solution in terms of total expected cost for simple instances of the problem. Next we present a series of results for the new policy. For example, we show that operational feasibility of a set of fixed routes can be checked in polynomial time, but identifying the optimal use of fixed routes is NP-complete. In the final part, we focus on a dynamic and stochastic routing problem, which arises when there are service level agreements in place between a distributor and its customers. Specifically, the distributor has to serve customer orders within two days after the order is received, but has the flexibility to choose the actual delivery day. However, future customer orders are unknown and are revealed dynamically through time. We develop heuristic and optimal policies for simple instances of the problem that use the stochastic information about future orders. We empirically compare the performance of the various policies with the performance of policies that do not use future information and with an offline optimal policy which has perfect information about future orders.

<p>The Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. Deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Residual Networks ease the training and have shown evidence that they can give good accuracy with considerable depth. Putting the best of Capsule Network and Residual Network together, we present Residual Capsule Network and 3-Level Residual Capsule Network, a framework that uses the best of Residual Networks and Capsule Networks. The conventional Convolutional layer in Capsule Network is replaced by skip connections like the Residual Networks to decrease the complexity of the Baseline Capsule Network and seven ensemble Capsule Network. We trained our models on MNIST and CIFAR-10 datasets and have seen a significant decrease in the number of parameters when compared to the Baseline models.</p>

Cette thèse présente une contribution sur les problèmes de contrôle de réseaux d'irrigations en tenant compte des contraintes de communication grâce à une approche multicouches d’intelligence distribuée. Les analyses détaillées de chaque couche avec les résultats analytiques et les simulations seront décrites dans les différents chapitres. Ils mettent l'accent sur l'intérêt de l'approche multicouches, plus précisément sur son efficacité et sa fiabilité pour la supervision, l'optimisation multi-objectifs et le contrôle coopératif distribué sur des systèmes complexes de transport d'eau.La première couche analysé est le réseau hydraulique composé de canaux d’écoulements à surface libre, de sous-réseaux maillés de tuyaux sous pression et des structures hydrauliques (pompes vannes, ..). En intégrant les équations de Saint-Venant pour décrire l’écoulement physique des fluides en surface libre et la méthode Lattice Boltzmann pour la simulation du fluide, nous obtenons un modèle non linéaire discret pour les canaux à surface libre. Les structures hydrauliques sont généralement traitées comme des limites internes des biefs (tronçons) et modélisées par des relations entre les variables de flux et de pression.Permettant l'échange d'informations entre les éléments du système de contrôle, le réseau de communication sera considéré comme la deuxième couche. La résolution des problèmes d’hétérogénéités des systèmes et des communications (par exemple les retards de diffusion dans le réseau, la perte de paquets, la consommation d'énergie) sera étudié en introduisant une architecture de réseau hybride avec un routage dynamique basé sur les exigences de Qualité de Service (QoS) des applications de contrôle. Pour le routage dynamique dans le réseau, une composition pondérée de certaines métriques standards est proposée afin que le protocole de routage utilisant cette métrique composite converge sans boucle avec une « route » optimum. Grâce à différents scénarios de simulation, plusieurs critères de performance du réseau ont été évalués. La comparaison des résultats de simulation permet de valider l'intérêt de cette approche de composition pour le routage dynamique.Une troisième couche propose un système de contrôle réactif optimal développé pour la régulation du réseau d'irrigation dans un modèle étendue à grande échelle : Distributed Cooperative Model Predictive Control (DCMPC). Cette partie aborde la mise en œuvre de différentes stratégies de contrôle (centralisées, décentralisées et distribuées) et intègre la communication coopérative entre les contrôleurs MPC locaux afin d’améliorer les performances global es du système. La gestion de la divergence dans l'échange d'informations entre les contrôleurs est considérée comme un problème de consensus et résolue en utilisant un protocole de consensus asynchrone. Cette approche du contrôle distribué basée sur le paradigme des systèmes multi-agents, fournit une solution garantissant que tous les contrôleurs aient une vue cohérente de certaines valeurs des données nécessaires pour le calcul de décision. Un cas d’application sur un canal d'irrigation est étudié dans les simulations. La comparaison des résultats de simulations valide les avantages de l'approche du contrôle distribué coopératif par rapport aux autres stratégies de contrôle. / This thesis presents control problems of irrigation network with communication constraints and a multi-layer approach to solve these problems in a distributed manner. Detailed discussions of each layer with analytical and simulation results are described throughout several chapters. They emphasize the potential interest of the multi-layer approach, more precisely its efficiency and reliability for supervision, multi-objective optimization and distributed cooperative control of complex water transport systems. Conventionally, the first layer to be considered is the hydraulic network composed of free-surface channels, hydraulic structures and mesh subnetwork of pressurized pipes. By coupling the Saint-Venant equations for describing the physics of free-surface fluid and the Lattice Boltzmann method for the fluid simulation, a discrete-time nonlinear model is obtained for channel reaches. The hydraulic structures are usually treated as internal boundaries of reaches and modeled by algebraic relationships between the flow and pressure variables. To enable the exchange of information among the control system’s components, a communication network is considered in the second layer. Solving challenging problems of heterogeneous devices and communication issues (e.g., network delay, packet loss, energy consumption) is investigated in this thesis by introducing a hybrid network architecture and a dynamic routing design based on Quality of Service (QoS) requirements of control applications. For network routing, a weighted composition of some standard metrics is proposed so that the routing protocol using the composite metric achieves convergence, loop-freeness and path-optimality properties. Through extensive simulation scenarios, different network performance criteria are evaluated. The comparison of simulation results can validate the interest of this composition approach for dynamic routing. Finally, the third layer introduces an optimal reactive control system developed for the regulatory control of large-scale irrigation network under a Distributed Cooperative Model Predictive Control (DCMPC) framework. This part discusses the implementation of different control strategies (e.g., centralized, decentralized, and distributed strategies) and how the cooperative communication among local MPC controllers can be included to improve the performance of the overall system. Managing divergent (or outdated) information exchange among controllers is considered in this thesis as a consensus problem and solved by an asynchronous consensus protocol. This approach based on the multi-agent system paradigm to distributed control requires each controller to agree with its neighbors on some data values needed during action computation. For simulations, a particular benchmark of an irrigation channel is considered. The comparison of simulation results validate the benefits of the distributed cooperative control approach over other control strategies.

La technologie des réseaux de capteurs sans fil démontre qu'elle peut être très utile dans de nombreuses applications. Ainsi chaque jour voit émerger de nouvelles réalisations dans la surveillance de notre environnement comme la détection des feux de forêt, l'approvisionnement en eau. Les champs d'applications couvrent aussi des domaines émergents et sensibles pour la population avec les soins aux personnes âgées ou les patients récemment opérés dans le cadre. L'indépendance des architectures RCSFs par rapport aux infrastructures existantes permet aux d'être déployées dans presque tous les sites afin de fournir des informations temporelles et spatiales. Dans les déploiements opérationnels le bon fonctionnement de l'architecture des réseaux de capteurs sans fil ne peut être garanti que si certains défis sont surmontés. La minisation de l'énergie consommée en fait partie. La limitation de la durée de vie des nœuds de capteurs est fortement couplée à l'autonomie de la batterie et donc à l'optimisation énergétique des nœuds du réseau. Nous présenterons plusieurs propositions à ces problèmes dans le cadre de cette thèse. En résumé, les contributions qui ont été présentées dans cette thèse, abordent la durée de vie globale du réseau, l'exploitation des messages de données redondantes et corrélées et enfin le fonctionnement nœud lui-même. Les travaux ont conduit à la réalisation d'algorithmes de routage hiérarchiques et de filtrage permettant la suppression des redondances. Ils s'appuient sur les corrélations spatio-temporelles des données mesurées. Enfin, une implémentation de ce réseau de capteurs multi-sauts intégrant ces nouvelles fonctionnalités est proposée. / Wireless sensor networks (WSNs) technology have been demonstrated to be a usefulmeasurement system for numerous bath indoor and outdoor applications. There is avast amount of applications that are operating with WSN technology, such asenvironmental monitoring, for forest fire detection, weather forecasting, water supplies, etc. The independence nature of WSNs from the existing infrastructure. Virtually, the WSNs can be deployed in any sort of location, and provide the sensor samples accordingly in bath time and space. On the contrast, the manual deployments can only be achievable at a high cost-effective nature and involve significant work. ln real-world applications, the operation of wireless sensor networks can only be maintained, if certain challenges are overcome. The lifetime limitation of the distributed sensor nodes is amongst these challenges, in order to achieve the energy optimization. The propositions to the solution of these challenges have been an objective of this thesis. ln summary, the contributions which have been presented in this thesis, address the system lifetime, exploitation of redundant and correlated data messages, and then the sensor node in terms of usability. The considerations have led to the simple data redundancy and correlated algorithms based on hierarchical based clustering, yet efficient to tolerate bath the spatio-temporal redundancies and their correlations. Furthermore, a multihop sensor network for the implementation of propositions with more features, bath the analytical proofs and at the software level, have been proposed.

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