A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Modelos para a determinação do risco da taxa de juro

Introdução PARTE 1 - Noções gerais e metodologias de medição baseadas nos diferenciais 1.1. Noção de risco 1.2. Principais riscos na actividade bancária 1.3. Modelos de quantificação do risco da taxa de juro 1.4. Modelos para quantificar o risco de reinvestimento 1.5. O modelo de diferencial de duração (DD) 1.6. Modelos para quantificar o risco de preço 1.7. Diferencial de duração da situação líquida 1.8. Vantagens/desvantagens dos modelos de duração (resultado e situação líquida) 1.9. Perspectivas e conclusão sobre os Modelos de Diferencial de Fundos e Duração PARTE II - Conceito de VAR 2.1 A noção de VAR (Valor em Risco) 2.2 Conceitos-chave dos modelos VAR 2.3 Fórmula de cálculo da duração modificada 2.4 A importância da duração para determinar a sensibilidade da taxa de juro 2.5 A problemática da convexidade 2.6 O conceitos de volatilidade 2.7 A agregação dos riscos 2.8 O tratamento do VAR com a matriz de correlação do andamento das taxas de juro 2.9 Esquemas sequenciais de cálculo da volatilidade preço - taxa de juro e VAR PARTE III - Casos práticos de VAR 3.1 As relações entre as taxas a prazo (forward) e as taxas à vista (spot) 3.2 Desenvolvimento de um caso prático 3.3 Cálculo do diferencial de duração e do VAR aplicado à situação líquida 3.4 Admissão de pressupostos 3.5 Os diferentes VAR´s 3.6 A importância do VAR no contexto de gestão de risco numa instituição 3.7 Os modelos de simulação estática e dinâmica PARTE IV - Situações especiais 4.1 O tratamento dos FRA´s e futuros 4.2 O tratamento das opções 4.3 O tratamento dos swap´s taxa de juro 4.4 A aplicação do modelo VAR aos riscos taxa de juro e cambial 4.5 A utilização dos modelos VAR na afectação do capital (RAROC) 4.6 A análise da instruçaõ nº 19/2005 ANEXOS Anexo 1 - Instrução nº 19/2005: risco de taxa de juro da carteira bancária Anexo 2 - Instrução nº 72/96: Princípios orientadores para ocontrolo do risco da taxa de juro Anexo 3 - Anexo V do Aviso nº 7/96 Conclusão Índice dos Quadros Bibliografia

Os Crimes fiscais: em especial, o crime de abuso de confiança (fiscal)

Dissertação para obtenção do Grau de Mestre em Contabilidade e Finanças Orientador: Professor Dr. António da Costa Oliveira

Soleus activity in post-stroke subjects: movement sequence from standing to sitting

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

Spatio-temporal alignment of pedobarographic image sequences

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

Comparative analysis of the ground reaction forces, during the support phase, in a group of pregnant women on their 3rd trimester of pregnancy and in a group of not pregnant women

PURPOSE: To analyze and compare the Ground Reaction Forces (GRF), during the stance phase of walking in pregnant women in the 3rd trimester of pregnancy, and non pregnant women. METHODS: 20 women, 10 pregnant and 10 non pregnant, voluntarily took part in this study. GRF were measured (1000 Hz) using a force platform (BERTEC 4060-15), an amplifier (BERTEC AM 6300) and an analogical-digital converter of 16 Bits (Biopac). RESULTS: The study showed that there were significant differences among the two groups concerning absolute values of time of the stance phase. In what concerns to the normalized values the most significant differences were verified in the maximums values of vertical force (Fz3, Fz1) and in the impulse of the antero-posterior force (Fy2), taxes of growth of the vertical force, and in the period of time for the antero-posterior force (Fy) be null. CONCLUSIONS: It is easier for the pregnant to continue forward movement (push-off phase). O smaller growth rates in what concerns to the maximum of the vertical force (Fz1) for the pregnant, can be associated with a slower speed of gait, as an adaptation strategy to maintain the balance, to compensate the alterations in the position of her center of gravity due to the load increase. The data related to the antero-posterior component of the force (Fy), shows that there is a significant difference between the pregnant woman’s left foot and right foot, which accuses a different functional behavior in each one of the feet, during the propulsion phase (TS).

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Electricity Markets Simulation: MASCEM Contributions to the Challenging Reality

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in sophisticated tools very helpful under this context. Some simulation tools have already been developed, some of them very interesting. However, at the present state it is important to go a step forward in Electricity Markets simulators as this is crucial for facing changes in Power Systems. This paper explains the context and needs of electricity market simulation, describing the most important characteristics of available simulators. We present our work concerning MASCEM simulator, presenting its features as well as the improvements being made to accomplish the change and challenging reality of Electricity Markets.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

Research on evaluation system of emergency disposal plan for power plant incidents

This paper starts with the analysis of the unusual inherence mechanism, from two aspects: accumulating and human error. We put forward twelve factors affected the decision of the emergency treatment plan in practice and summarized the evaluation index system combining with literature data. Then we screened out eighteen representative indicators by used the FDM expert questionnaire in the first phase. Hereafter, we calculated the weight of evaluation index and sorted them by the FAHP expert questionnaire, and came up with the frame of the evaluation rule by combined with the experience. In the end, the evaluation principles are concluded.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

The darmstadt challenge (the turing test revisited)

Significant work has been done in the areas of Pervcomp/Ubicomp Smart Environments with advances on making proactive systems, but those advances have not made these type of systems accurately proactive. On the other hand a great deal is needed to make systems more sensible/sensitive and trustable (both in terms of reliability and privacy). We put forward the thesis that a more integral and social-aware sort of intelligence is needed to effectively interact, decide and act on behalf of people’s interest and that a way to test how effective systems are achieving these desirable behaviour is needed as a consequence. We support our thesis by providing examples on how to measure effectiveness in variety of different environments.

A importância do relacionamento bancário no financiamento das PME

Dissertação de Mestrado em Finanças Empresariais