A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

In this work, we investigate job-shop problems where limited capacity buffers to store jobs in non-processing periods are present. In such a problem setting, after finishing processing on a machine, a job either directly has to be processed on the following machine or it has to be stored in a prespecified buffer. If the buffer is completely occupied the job may wait on its current machine but blocks this machine for other jobs. Besides a general buffer model,also specific configurations are considered.The key issue to develop fast heuristics for the job-shop problem with buffers is to find a compact representation of solutions. In contrast to the classical job-shop problem,where a solution may be given by the sequences of the jobs on the machines, now also the buffers have to be incorporated in the solution representation. In this work, we propose two solution representations for the job-shop problem with buffers. Furthermore, we investigate whether the given solution representations can be simplified for specific buffer configurations. For the general buffer configuration it is shown that an incorporation of the buffers in the solution representation is necessary, whereas for specific buffer configurations possible simplifications are presented. Based on the given solution representations we develop local search heuristics in the second part of this work. Therefore, the well-known block approach for the classical job-shop problem is generalized to the job-shop problem with specific buffer configurations.

In this thesis scheduling problems with transportation aspects are studied. Classical scheduling models for problems with multiple operations are the so-called shop-scheduling models. In these models jobs consisting of different operations have to be planned on certain machines in such a way that a given objective function is minimized. Each machine may process at most one operation at a time and operations belonging to the same job cannot be processed simultaneously. We generalize these classical shop-scheduling problems by assuming that the jobs additionally have to be transported between the machines. This transportation has to be done by robots which can handle at most one job at a time. Besides transportation times which occur for the jobs during their transport, also empty moving times are considered which arise when a robot moves empty from one machine to another. Two types of problems are distinguished: on the one hand, problems without transportation conflicts (i.e. each transportation can be performed without delay), and on the other hand, problems where transportation conflicts may arise due to a limited capacity of transport robots. In the first part of this thesis several new complexity results are derived for flow-shop problems with a single robot. Since very special cases of these problems are already NP-hard, in the second part of this thesis some techniques are developed for dealing with these hard problems in practice. We concentrate on the job-shop problem with a single robot and the makespan objective. At first we study the subproblem which arises for the robot when some scheduling decisions for the machines have already been made. The resulting single-machine problem can be regarded as a generalization of the traveling salesman problem with time windows where additionally minimal time-lags between certain jobs have to be respected and the makespan has to be minimized. For this single-machine problem we adapt immediate selection techniques used for other scheduling problems and calculate lower bounds based on linear programming and the technique of column generation. On the other hand, to determine upper bounds for the single-machine problem we develop an efficient local search algorithm which finds good solutions in reasonable time. This algorithm is integrated into a local search algorithm for the job-shop problem with a single robot. Finally, the proposed algorithms are tested on different test data and computational results are presented.