Made available in DSpace on 2014-07-29T14:57:52Z (GMT). No. of bitstreams: 1 Dissertacao Diego Galindo Pecin.pdf: 448272 bytes, checksum: 755b351108c1082bc3bdb084b7add6ec (MD5) Previous issue date: 2010-02-23 / This dissertation addresses the optimization of the Elementary Shortest Path Problem with a Capacity Constraint (ESPPCC) and describes algorithms for its resolution that make use of concepts such as Label-Setting, Bidirectional Dynamic Programming and Decremental State Space Relaxation. These algorithms were used in a robust CVRP s Branch-and- Cut-and-Price framework as the column generation mechanism. The resulting BCP was used to obtain results (lower bounds, processing time and the number of branching nodes generated) to several CVRP s test instances. These results are compared with previous ones obtained with the original BCP, which is based on k-cycle elimination. Elementary routes are also explored in a route enumeration context, which allows the enumeration of all possible relevant elementary routes, i.e., all routes that have a chance of being part of an optimal CVRP s solution. If the number of relevant routes is not too large (say, in the range of tenths of thousands), the overall problem may be solved by feeding a general MIP solver with a set-partition formulation containing only those routes. If this set-partition can be solved, the optimal solution will be found and no branch will be necessary. Sometimes this leads to very significant speedups when compared to traditional branch strategies. / Esta dissertação aborda o Problema do Caminho Elementar Mínimo com Restrição de Capacidade (ESPPCC Elementary Shortest Path Problem with a Capacity Constraint) e descreve algoritmos para a sua resolução que fazem uso de conceitos tais como Correção de Rótulos, Programação Dinâmica Bidirecional e Relaxação Decrescente do Espaço de Estados. Esses algoritmos foram usados como geradores de rotas elementares no subproblema de geração de colunas de um algoritmo BCP robusto para o CVRP. Os resultados (limites inferiores, tempo de processamento e número de nós gerados) obtidos, para algumas instâncias de teste do CVRP, são comparados aos obtidos na versão original desse algoritmo BCP, que utiliza rotas não elementares sem 3-ciclos ou 4-ciclos. Rotas elementares também são exploradas em um contexto de enumeração para o CVRP, a qual permite obter rotas (usando um critério baseado em limites e em custo reduzido) que possuem uma chance de pertencer a uma solução ótima. Se o número de rotas não for muito grande (na ordem de poucos de milhares), então todo o problema pode ser resolvido como um problema de particionamento de conjuntos contendo apenas tais rotas. Algumas vezes isso acelera o algoritmo Branch-and-Bound consideravelmente, quando comparado com estratégias tradicionais de particionamento (branching), já que muitos nós da árvore podem ser resolvidos sem a geração de novos nós.

This dissertation considers daily operations management for a fleet of trucks providing container pickup and delivery service to a port. Truck congestion at access points for ports may lead to serious inefficiencies in drayage operations, and the resultant cost impact to the intermodal supply chain can be significant. Recognizing that port congestion is likely to continue to be a major problem for drayage operations given the growing volume of international containerized trade, this research seeks to develop optimization approaches for maximizing the productivity of drayage firms operating at congested seaports. Specifically, this dissertation addresses two daily drayage routing and scheduling problems. In the first half of this dissertation, we study the problem of managing a fleet of trucks providing container pickup and delivery service to a port facility that experiences different access wait times depending on the time of day. For this research, we assume that the wait time can be estimated by a deterministic function. We develop a time-constrained routing and scheduling model for the problem that incorporates the time-dependent congestion delay function. The model objective is to find routes and schedules for drayage vehicles with minimum total travel time, including the waiting time at the entry to the port due to congestion. We consider both exact and heuristic solution approaches for this difficult optimization problem. Finally, we use the framework to develop an understanding of the potential impact of congestion delays on drayage operations, and the value of planning with accurate delay information. In the second half of this dissertation, we study methods for managing a drayage fleet serving a port with an appointment-based access control system. Responding to growing access congestion and its resultant impacts, many U.S. port terminals have implemented appointment systems, but little is known about the impact of such systems on drayage productivity. To address this knowledge gap, we develop a drayage operations optimization approach based on a column generation integer programming heuristic that explicitly models a time-slot port access control system. The approach determines pickup and delivery sequences with minimum transportation cost. We use the framework to develop an understanding of the potential efficiency impacts of access appointment systems on drayage operations. Findings indicate that the set of feasible drayage tasks and the fleet size required to complete them can be quite sensitive to small changes in time-slot access capacities at the port.