Orientador: Rubén Augusto Romero Lázaro / Resumo: Neste trabalho, consideram-se a análise teórica e a implementação computacional dos algoritmos Primal Simplex Canalizado (PSC) e Dual Simplex Canalizado (DSC) especializados. Esses algoritmos foram incorporados em um algoritmo Branch and Bound (B&B) de modo a resolver o problema de Planejamento da Expansão de Sistemas de Transmissão (PEST). Neste caso, o problema PEST foi modelado usando os chamados modelo de Transportes e modelo Linear Disjuntivo (LD), o que produz um problema de Programação Linear Inteiro Misto (PLIM). O algoritmo PSC é utilizado na resolução do problema de Programação Linear (PL) inicial após desconsiderar a restrição de integralidade do problema PLIM original. Juntamente com o algoritmo PSC, foi implementada uma estratégia para reduzir o número de variáveis artificiais adicionadas ao PL, consequentemente reduzindo o número de iterações do algoritmo PSC. O algoritmo DSC é utilizado na reotimização eficiente dos subproblemas gerados pelo algoritmo B&B, através do quadro ótimo do PL inicial, excluindo, assim, a necessidade da resolução completa de cada subproblema e, consequentemente, reduzindo o consumo de processamento e memória. Nesta pesquisa, é apresentada uma nova proposta de otimização, e, consequentemente, a implementação computacional usando a linguagem de programação FORTRAN que opera independentemente de qualquer solver. / Doutor

Submitted by JEFERSON BACK VANDERLINDE null (jefersonbv@yahoo.com.br) on 2017-11-01T16:38:25Z No. of bitstreams: 1 jeferson_tese_final_20171101.pdf: 4860852 bytes, checksum: 2f99c37969be3815f82b1b4455a40230 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-11-13T15:38:34Z (GMT) No. of bitstreams: 1 vanderlinde_jb_dr_ilha.pdf: 4860852 bytes, checksum: 2f99c37969be3815f82b1b4455a40230 (MD5) / Made available in DSpace on 2017-11-13T15:38:34Z (GMT). No. of bitstreams: 1 vanderlinde_jb_dr_ilha.pdf: 4860852 bytes, checksum: 2f99c37969be3815f82b1b4455a40230 (MD5) Previous issue date: 2017-09-06 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho, consideram-se a análise teórica e a implementação computacional dos algoritmos Primal Simplex Canalizado (PSC) e Dual Simplex Canalizado (DSC) especializados. Esses algoritmos foram incorporados em um algoritmo Branch and Bound (B&B) de modo a resolver o problema de Planejamento da Expansão de Sistemas de Transmissão (PEST). Neste caso, o problema PEST foi modelado usando os chamados modelo de Transportes e modelo Linear Disjuntivo (LD), o que produz um problema de Programação Linear Inteiro Misto (PLIM). O algoritmo PSC é utilizado na resolução do problema de Programação Linear (PL) inicial após desconsiderar a restrição de integralidade do problema PLIM original. Juntamente com o algoritmo PSC, foi implementada uma estratégia para reduzir o número de variáveis artificiais adicionadas ao PL, consequentemente reduzindo o número de iterações do algoritmo PSC. O algoritmo DSC é utilizado na reotimização eficiente dos subproblemas gerados pelo algoritmo B&B, através do quadro ótimo do PL inicial, excluindo, assim, a necessidade da resolução completa de cada subproblema e, consequentemente, reduzindo o consumo de processamento e memória. Nesta pesquisa, é apresentada uma nova proposta de otimização, e, consequentemente, a implementação computacional usando a linguagem de programação FORTRAN que opera independentemente de qualquer solver. / In this research, the theoretical analysis and computational implementation of the specialized dual simplex algorithm (DSA) and primal simplex algorithm (PSA) for bounded variables is considered. These algorithms have been incorporated in a Branch and Bound (B&B) algorithm to solve the Transmission Network Expansion Planning (TNEP) problem. In this case, the TNEP problem is modeled using transportation model and linear disjunctive model (DM), which produces a mixed-integer linear programming (MILP) problem. After relaxing the integrality of investment variables of the original MILP problem, the PSA is used to solve the initial linear programming (LP) problem. Also, it has been implemented a strategy in PSA to reduce the number of artificial variables which are added into the LP problem, and consequently reduces the number of iterations of PSA. Through optimal solution of the initial LP, the DSA is used in efficient reoptimization of subproblems, resulting from the B&B algorithm, thus excludes the need for complete resolution of each subproblems, which results reducing the CPU time and memory consumption. This research presents the implementation of the proposed approach using the FORTRAN programming language which operates independently and does not use any commercial solver.

This dissertation focuses on selected issues in regard to the mathematical modeling of electricity markets. In a first step the interrelations of electric power market modeling are highlighted a crossroad between operations research, applied economics, and engineering. In a second step the development of a large-scale continental European economic engineering model named ELMOD is described and the model is applied to the issue of wind integration. It is concluded that enabling the integration of low-carbon technologies appears feasible for wind energy. In a third step algorithmic work is carried out regarding a game theoretic model. Two approaches in order to solve a discretely-constrained mathematical program with equilibrium constraints using disjunctive constraints are presented. The first one reformulates the problem as a mixed-integer linear program and the second one applies the Benders decomposition technique. Selected numerical results are reported.

Dans les chaînes d’approvisionnement, les producteurs font souvent appel à des entreprises de transport pour livrer leurs marchandises. Cela peut entraîner une concurrence entre les transporteurs qui cherchent à maximiser leurs revenus individuels en desservant un produc- teur. Dans ce travail, nous considérons de telles situations où aucun transporteur ne peut garantir la livraison de la source à la destination en raison de son activité dans une région restreinte (par exemple, une province) ou de la flotte de transport disponible (par exemple, uniquement le transport aérien), pour ne citer que quelques exemples. La concurrence est donc liée à l’expansion de la capacité de transport des transporteurs. Le problème décrit ci-dessus motive l’étude du jeu d’expansion de réseau multi-agent joué sur un réseau appartenant à de multiples transporteurs qui choisissent la capacité de leurs arcs. Simultanément, un client cherche à maximiser le flux qui passe par le réseau en décidant de la politique de partage qui récompense chacun des transporteurs. Le but est de déterminer un équilibre de Nash pour le jeu, en d’autres termes, la strategie d’extension de capacité et de partage la plus rationnelle pour les transporteurs et le client, respectivement. Nous rappelons la formulation basée sur les arcs proposée dans la littérature, dont la solution est l’équilibre de Nash avec le plus grand flux, et nous identifions ses limites. Ensuite, nous formalisons le concept de chemin profitable croissant et nous montrons son utilisation pour établir les conditions nécessaires et suffisantes pour qu’un vecteur de stratégies soit un équilibre de Nash. Ceci nous conduit à la nouvelle formulation basée sur le chemin. Enfin, nous proposons un renforcement du modèle basé sur les arcs et une formulation hybride arc- chemin. Nos résultats expérimentaux soutiennent la valeur des nouvelles inégalités valides obtenues à partir de notre caractérisation des équilibres de Nash avec des chemins croissants rentables. Nous concluons ce travail avec les futures directions de recherche pavées par les contributions de cette thèse. / In supply chains, manufacturers often use transportation companies to deliver their goods. This can lead to competition among carriers seeking to maximize their individual revenues by serving a manufacturer. In this work, we consider such situations where no single carrier can guarantee delivery from source to destination due to its operation in a restricted region (e.g., a province) or the available transportation fleet (e.g., only air transportation), to name a few examples. Therefore, competition is linked to the expansion of transportation capacity by carriers. The problem described above motivates the study of the multi-agent network expansion game played over a network owned by multiple transporters who choose their arcs’ capacity. Simultaneously, a customer seeks to maximize the flow that goes through the network by deciding the sharing policy rewarding each of the transporters. The goal is to determine a Nash equilibrium for the game, in simple words, the most rational capacity expansion and sharing policy for the transporters and the customer, respectively. We recap the arc-based formulation proposed in literature, whose solution is the Nash equilibirum with the largest flow, and we identify its limitations. Then, we formalize the concept of profitable increasing path and we show its use to establish necessary and sufficient conditions for a vector of strategies to be a Nash equilibrium. This lead us to the first path-based formulation. Finally, we propose a strengthening for the arc-based model and a hybrid arc-path formulation. Our experimental results support the value of the new valid inequalities obtained from our characterization of Nash equilibria with profitable increasing paths. We conclude this work with the future research directions paved by the contributions of this thesis.