Applications of mathematical network theory

This thesis is a collection of papers on a variety of optimization problems where network structure can be used to obtain efficient algorithms. The considered applications range from the optimization of radiation treatment plkans in cancer therapy to maintenance planning for maximizing the throughput in bulk good supply chains

On the minimum cardinality problem in intensity modulated radiotherapy

The thesis examines an optimisation problem that appears in the treatment planning of intensity modulated radiotherapy. An approach is presented which solved the optimisation problem in question while also extending the approach to execute in a massively parallel environment. The performance of the approach presented is among the fastest available

Subgradient Optimization Methods in Integer Programming with an Application to a Radiation Therapy Problem

The thesis deals with the subgradient optimization methods which are serving to solve nonsmooth optimization problems. We are particularly concerned with solving large-scale integer programming problems using the methodology of Lagrangian relaxation and dualization. The goal is to employ the subgradient optimization techniques to solve large-scale optimization problems that originated from radiation therapy planning problem. In the thesis, different kinds of zigzagging phenomena which hamper the speed of the subgradient procedures have been investigated and identified. Moreover, we have established a new procedure which can completely eliminate the zigzagging phenomena of subgradient methods. Procedures used to construct both primal and dual solutions within the subgradient schemes have been also described. We applied the subgradient optimization methods to solve the problem of minimizing total treatment time of radiation therapy. The problem is NP-hard and thus far there exists no method for solving the problem to optimality. We present a new, efficient, and fast algorithm which combines exact and heuristic procedures to solve the problem

Desenvolvimento e avaliação de matheurísticas para o combinado problema do posicionamento dos feixes e distribuição de dose no planejamento de radioterapia

Orientador : Neida Maria Patias VolpiTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 08/07/2016Inclui referências : f. 71-75Área de concentraçãoResumo: O processo de planejamento de radioterapia é um fator essencial para garantir o nível máximo de eficiência do tratamento subsequente. Neste planejamento, há pelo menos dois problemas de decisão que podem ser modelados e resolvidos utilizando técnicas de Pesquisa Operacional. Estes incluem a melhor posição para emissão do feixe (problema do posicionamento dos feixes) e a quantidade ótima da dose que deve ser entregue através de cada feixe (problema da distribuição de dose). Esta tese apresenta um modelo matemático para otimizar concomitantemente os problemas do posicionamento dos feixes e da distribuição de dose, na presença de múltiplos objetivos. Três matheurísticas são propostas para resolver casos realistas que são de grande escala. As matheurísticas usam, respectivamente, Algoritmos Genéticos, Busca Tabu e Busca em Vizinhança Variável e são, portanto, denominadas GArad, TSrad e VNSrad. O desempenho das metodologias propostas é avaliado em dois tipos de instâncias de câncer na região da próstata, que envolvem um único corte de tomografia computadorizada (CT) e um conjunto de cortes de CT (problema 3D). Para o problema em um único corte de CT, os resultados das matheurísticas propostas são comparados com a solução ótima obtida por método exato. Em ambas instâncias, avaliaram-se os resultados em relação à cobertura de dose no tumor, e aos limites percentuais de dose nos órgãos de risco, além de avaliar a performance das metodologias em diferentes tempos computacionais. No geral, as metodologias fornecem uma solução para os problemas do posicionamento dos feixes e distribuição de dose, e, além disso, são metodologias flexíveis para considerar as necessidades específicas do paciente. Palavras-chaves: Saúde; Radioterapia; Otimização; Matheurística; Algoritmo Genético; Busca Tabu; Busca em Vizinhança Variável.Abstract: Radiotherapy planning is a vital component in ensuring the maximum level of effectiveness of the subsequent treatment. In the planning task, there are at least two connected decision problems that can be modelled and solved using Operational Research techniques. These include the best position of the radiotherapy machine (beam angle problem) and the optimal quantity of the dose that has to be delivered through each beam (dose distribution problem). This thesis presents a mathematical optimisation model for solving the combined beam angle and dose distribution problem in the presence of multiple objectives. Three matheuristics are developed to solve realistic large-scale instances. The matheuristics use Genetic Algorithms, Tabu Search and Variable Neighbourhood Search and are hence termed GArad, TSrad and VNSrad, respectively. The performance of the proposed methods is assessed on two prostate cancer instances, namely a single computed tomography (CT) slice and a set of CT slices (3D problem). For the single-slice problem, the results of the proposed matheuristics are compared to the optimal solutions obtained by an exact method where the experiments show that the proposed methods are able to achieve optimality or to produce a relatively small deviation. For the multi-slice problem, the computational experiments show that the proposed methods produce viable solutions which can be attained in a reasonable computational time. Overall, the methodologies can provide a solution for beam angle and dose distribution problems, and besides that they are flexible to consider the patient needs. Key-words: Healthcare; Radiotherapy; Optimisation; Matheuristic; Genetic Algorithm; Tabu Search; Variable Neighbourhood Search