The less-than-truckload (LTL) transportation industry is highly competitive with low profit margins. Carriers in this industry strive to reduce costs and improve customer service to remain profitable. LTL carriers rely on a network of hubs and service centers to transfer freight. A hub is typically a cross docking terminal in which shipments from inbound trailers are unloaded and reassigned and consolidated onto outbound trailers going to the correct destinations. Freight handling in a hub is labor intensive, and workers must quickly transfer freight during a short time period to support customer service levels. Reducing shipment transfer time in hubs offers the opportunity to reduce labor costs, improve customer service, and increase competitive advantages for carriers. This research focuses on improving the efficiency of hub operations in order to decrease the handling costs and increase service levels for LTL carriers. Specifically, the following two decision problems are investigated: (1) assigning trailers to dock doors to minimize the total time required to transfer shipments from inbound trailers to destination trailers and (2) sequencing unloading and loading of freight to minimize the time required by dock employees. The trailer-to-door assignment problem is modeled as a Quadratic Assignment Problem (QAP). Both semi-permanent and dynamic layouts for the trailer-to-door assignment problem are evaluated. Improvement based heuristics, including pair-wise exchange, simulated annealing, and genetic algorithms, are explored to solve the trailer-to-door assignment problem. The freight sequencing problem is modeled as a Rural Postman Problem (RPP). A Balance and Connect Algorithm (BCA) and an Assign First and Route Second Algorithm (AFRSA) are investigated and compared to Balanced Trailer-at-a-Time (BTAAT), Balanced Trailer-at-a-Time with Offloading (BTAATWO), and Nearest Neighbor (NN). The heuristics are evaluated using data from two LTL carriers. For these data sets, both the total travel distance and the transfer time of hub operations are reduced using a dynamic layout with efficient freight sequencing approaches, such as the Balance and Connect Algorithm (BCA), the Balanced Trailer-at-a-Time with Offloading (BTAATWO), and the Nearest Neighbor (NN). Specifically, with a dynamic layout, the BCA reduces travel distance by 10% to 27% over BTAAT and reduces the transfer time by 17% to 68% over BTAAT. A simulation study is also conducted for hub operations in a dynamic and stochastic environment. The solutions from the door assignment and freight sequencing approaches are evaluated in a simulation model to determine their effectiveness in this environment. The simulation results further demonstrate that the performance measures of hub operations are improved using a dynamic layout and efficient freight sequencing approaches. The main contributions of this research are the integer programming models developed for the freight sequencing problem (FSP), based on the Rural Postman Problem (RPP). This is the first known application of the RPP for the FSP. Efficient heuristics are developed for the FSP for a single worker and for multiple workers. These heuristics are analyzed and compared to previous research using industry data. / Ph. D.

La gestion d’une plate-forme selon une stratégie de cross dock est un processus logistique efficace et dynamique qui vise à transférer directement les produits d'un fournisseur à un client. Cette thèse aborde les problèmes d'affectation aux portes et de gestion des ressources sous contraintes de fenêtres du temps dans un cross dock spécifique. Les problèmes sont formulés comme des modèles de programmation mathématique mixte (MIP). L’objectif est de minimiser la somme de la distance parcourue dans l’entrepôt et du coût qui contient les coûts liés aux ressources et un coût de pénalité. Une heuristique basée sur les algorithmes génétiques est proposée pour résoudre ce problème. Deux méthodes de réparation des gènes sont décrites pour rendre les solutions irréalisables réalisables. Les résultats montrent que l’algorithme génétique surpasse la résolution du modèle MIP à l’aide du solveur CPLEX en un temps donné, pour des instances de taille moyenne et de grande taille. Afin de décrire le comportement et de recueillir des informations pertinentes sur la gestion en cross docks, nous proposons un modèle basé sur réseau de Pétri. Un modèle par réseau de Pétri est construit et la simulation est réalisée avec le logiciel Tina. Par simulation, avec différents nombres de ressources, nous obtenons des temps pertinents pour améliorer les fenêtres de temps originales, le makespan de chacun des postes de travail, l'intervalle de temps libre dans le cross dock et la quantité de ressources disponible à chaque période de temps, ce qui peut fournir des conseils utiles à la gestion des ressources. A partir des résultats de la simulation, la formulation MIP est améliorée. / Cross docking is an efficient and dynamic logistic process that directly transfers goods from a supplier to a customer. This thesis addresses the door assignment and resource management problem with truck time windows constraints for a specific cross dock. The problems are formulated as mixed integer programming (MIP) models. The objective is to minimize the weighted sum of the total travel distance and cost which includes labor cost and penalty cost. A heuristic based on genetic algorithm (GA) is developed to solve the problems. Two gene repair methods are proposed to repair infeasible solutions. The computational results show that genetic algorithms outperforms the solution of MIP model with CPLEX in a given CPU time, for medium and large size instances, and that the second gene repair method outperforms the first one. In order to describe the behavior and gather information on the cross dock, a model based on Petri net is built to study the cross docks and simulations are carried out with Tina. The simulation results for different resource number lead us to obtain the relevant times to improve the original time windows, the makespan at each work station, the free time interval in the cross dock and the free resource number at each time period, which provide relevant information on the resource management. Besides, according to the simulation results, the original MIP formulations are improved. Then we propose a new MIP formulation, which determine not only door assignment, but also resources at each time period at each work station. Computational results reveal that the new MIP model control resources in cross dock more efficiently and outperforms the first model.