An origin-based model for unique shortest path routing

Link weights are the main parameters of shortest path routing protocols, the most commonly used protocols for IP networks. The problem of optimally setting link weights for unique shortest path routing is addressed. Due to the complexity of the constraints involved, there exist challenges to formulate the problem in such a way based on which a more efficient solution algorithm than the existing ones may be developed. In this paper, an exact formulation is first introduced and then mathematically proved correct. It is further illustrated that the formulation has advantages over a prior one in terms of both constraint structure and model size for a proposed decomposition method to solve the problem

O problema do caminho mais curto com restrições de capacidade

Mestrado em Matemática e AplicaçõesNeste trabalho estuda-se o problema do caminho mais curto com capacidades (PCMCRC). O PCMCRC é uma variante do problema do caminho mais curto onde existe uma restrição de capacidade associada aos arcos. Este problema tem variadas aplicações, nomeadamente na área das telecomunicações e no planeamento de rotas de veículos. Na sua forma geral o PCMCRC é NP-difícil. É feita uma descrição do problema, uma breve referência às principais técnicas de resolução e é proposto um novo algoritmo heurístico baseado na relaxação da restrição de capacidade. É efectuado um estudo computacional com o objectivo de identificar as instâncias mais difíceis do PCMCRC e, também, de testar o novo algoritmo.This work studies the shortest path problem with capacities (SPPC). The SPPC is a variation of the shortest path problem, where there is a capacity constraint associated with the arcs. This problem has multiple applications in areas such as telecommunications and traffic routing planning. In it’s general form, it’s a NP-hard problem. It is made a description of the problem, a slight reference to the main resolution techniques, and it’s proposed a new heuristic algorithm, based on the relaxation of the capacity constraint. It is reported a computational study in order to identify the hard instances for the SPPC and in order to test the new algorithm

Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms

Optimizing and Reoptimizing: tackling static and dynamic combinatorial problems

As suggested by the title, in this thesis both static and dynamic problems of Operations Research will be addressed by either designing new procedures or adapting well-known algorithmic schemes. Specifically, the first part of the thesis is devoted to the discussion of three variants of the widely studied Shortest Path Problem, one of which is defined on dynamic graphs. Namely, first the Reoptimization of Shortest Paths in case of multiple and generic cost changes is dealt with an exact algorithm whose performance is compared with Dijkstra's label setting procedure in order to detect which approach has to be preferred. Secondly, the k-Color Shortest Path Problem is tackled. It is a recent problem, defined on an edge-constrained graph, for which a Dynamic Programming algorithm is proposed here; its performance is compared with the state of the art solution approach, namely a Branch & Bound procedure. Finally, the Resource Constrained Clustered Shortest Path Tree Problem is presented. It is a newly defined problem for which both a mathematical model and a Branch & Price procedure are detailed here. Moreover, the performance of this solution approach is compared with that of CPLEX solver. Furthermore, in the first part of the thesis, also the Path Planning in Urban Air Mobility, is discussed by considering both the definition of the Free-Space Maps and the computation of the trajectories. For the former purpose, three different but correlated discretization methods are described; as for the latter, a two steps resolution, offline and online, of the resulting shortest path problems is performed. In addition, it is checked whether the reoptimization algorithm can be used in the online step. In the second part of this thesis, the recently studied Additive Manufacturing Machine Scheduling Problem with not identical machines is presented. Specifically, a Reinforcement Learning Iterated Local Search meta-heuristic featuring a Q-learning Variable Neighbourhood Search is described to solve this problem and its performance is compared with the one of CPLEX solver. It is worthwhile mentioning that, for each of the proposed approaches, a thorough experimentation is performed and each Chapter is equipped with a detailed analysis of the results in order to appraise the performance of the method and to detect its limits