The prominence of social responsibility disclosure in Portuguese companies’ web pages

This study investigates Portuguese companies’ use of the Internet to communicate social responsibility information, and the factors that affect this use. It examines the characteristics of companies that influence the prominence of social responsibility information on the Internet. Firm-specific factors that explain SRD by companies operating in a European country in which capital market fund raising is not regarded to be an important source of financing are analysed. The results are interpreted through the lens of a “political economy” framework which combines stakeholder and legitimacy theories perspectives, according to which companies disclose social responsibility information to present a socially responsible image so that they can legitimise their behaviours to their stakeholder groups and influence the external perception of reputation. Results suggest that a theoretical framework combining stakeholder and legitimacy theories may provide an explanatory basis for SRD by Portuguese companies. However, this study does not provide us with enough evidence to determine that the prominence given to CSR activities by Portuguese companies in their websites is linked to relationships with their stakeholders

Abordagem ao risco de crédito no âmbito do acordo de Basileia III em Portugal

Dissertação para a obtenção do Grau de Mestre em Contabilidade e Finanças Orientador: Mestre Adalmiro Álvaro Malheiro de Castro Andrade Pereira

MASDSheGATS – Scheduling System for Dynamic Manufacturing Environments

This chapter addresses the resolution of scheduling in manufacturing systems subject to perturbations. The planning of Manufacturing Systems involves frequently the resolution of a huge amount and variety of combinatorial optimisation problems with an important impact on the performance of manufacturing organisations. Examples of those problems are the sequencing and scheduling problems in manufacturing management, routing and transportation, layout design and timetabling problems.

Optimal location of gas supply units in natural gas system network

Natural gas industry has been confronted with big challenges: great growth in demand, investments on new GSUs – gas supply units, and efficient technical system management. The right number of GSUs, their best location on networks and the optimal allocation to loads is a decision problem that can be formulated as a combinatorial programming problem, with the objective of minimizing system expenses. Our emphasis is on the formulation, interpretation and development of a solution algorithm that will analyze the trade-off between infrastructure investment expenditure and operating system costs. The location model was applied to a 12 node natural gas network, and its effectiveness was tested in five different operating scenarios.

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

A marcha: a influência do Gerador de Padrão Central

A marcha é influenciada por um conjunto de factores que resultam da interacção e organização própria de sistemas neurais e mecânicos, entre os quais, da dinâmica músculo-esquelética, da modulação pelos centros nervosos superiores, pela modulação aferente e é, também, assumida como sendo controlada pelo Gerador de Padrão Central (GPC), que se define como um programa central baseado num circuito espinal geneticamente determinado, capaz de produzir um ritmo associado a cada marcha. Apresenta-se como objectivo deste trabalho abordar quais os modelos explicativos para o funcionamento do GPC e qual a evidência científica, que continua a ter muitas divergências.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

A marcha: a influência do Gerador de Padrão Central

A marcha é influenciada por um conjunto de factores que resultam da interacção e organização própria de sistemas neurais e mecânicos, entre os quais, da dinâmica músculo-esquelética, da modulação pelos centros nervosos superiores, pela modulação aferente e é, também, assumida como sendo controlada pelo Gerador de Padrão Central (GPC), que se define como um programa central baseado num circuito espinal geneticamente determinado, capaz de produzir um ritmo associado a cada marcha. Apresenta-se como objectivo deste trabalho abordar quais os modelos explicativos para o funcionamento do GPC e qual a evidência científica, que continua a ter muitas divergências.

Aspectos de aprendizagem em optimização

A optimização e a aprendizagem em Sistemas Multi-Agente são consideradas duas áreas promissoras mas relativamente pouco exploradas. A optimização nestes ambientes deve ser capaz de lidar com o dinamismo. Os agentes podem alterar o seu comportamento baseando-se em aprendizagem recente ou em objectivos de optimização. As estratégias de aprendizagem podem melhorar o desempenho do sistema, dotando os agentes da capacidade de aprender, por exemplo, qual a técnica de optimização é mais adequada para a resolução de uma classe particular de problemas, ou qual a parametrização é mais adequada em determinado cenário. Nesta dissertação são estudadas algumas técnicas de resolução de problemas de Optimização Combinatória, sobretudo as Meta-heurísticas, e é efectuada uma revisão do estado da arte de Aprendizagem em Sistemas Multi-Agente. É também proposto um módulo de aprendizagem para a resolução de novos problemas de escalonamento, com base em experiência anterior. O módulo de Auto-Optimização desenvolvido, inspirado na Computação Autónoma, permite ao sistema a selecção automática da Meta-heurística a usar no processo de optimização, assim como a respectiva parametrização. Para tal, recorreu-se à utilização de Raciocínio baseado em Casos de modo que o sistema resultante seja capaz de aprender com a experiência adquirida na resolução de problemas similares. Dos resultados obtidos é possível concluir da vantagem da sua utilização e respectiva capacidade de adaptação a novos e eventuais cenários.

Rapid determination of tartaric acid in wines

A flow-spectrophotometric method is proposed for the routine determination of tartaric acid in wines. The reaction between tartaric acid and vanadate in acetic media is carried out in flowing conditions and the subsequent colored complex is monitored at 475 nm. The stability of the complex and the corresponding formation constant are presented. The effect of wavelength and pH was evaluated by batch experiments. The selected conditions were transposed to a flowinjection analytical system. Optimization of several flow parameters such as reactor lengths, flow-rate and injection volume was carried out. Using optimized conditions, a linear behavior was observed up to 1000 µg mL-1 tartaric acid, with a molar extinction coefficient of 450 L mg-1 cm-1 and ± 1 % repeatability. Sample throughput was 25 samples per hour. The flow-spectrophotometric method was satisfactorily applied to the quantification of tartaric acid (TA) in wines from different sources. Its accuracy was confirmed by statistical comparison to the conventional Rebelein procedure and to a certified analytical method carried out in a routine laboratory.

A new MILP based approach for unit commitment in power production planning

This paper presents a complete, quadratic programming formulation of the standard thermal unit commitment problem in power generation planning, together with a novel iterative optimisation algorithm for its solution. The algorithm, based on a mixed-integer formulation of the problem, considers piecewise linear approximations of the quadratic fuel cost function that are dynamically updated in an iterative way, converging to the optimum; this avoids the requirement of resorting to quadratic programming, making the solution process much quicker. From extensive computational tests on a broad set of benchmark instances of this problem, the algorithm was found to be flexible and capable of easily incorporating different problem constraints. Indeed, it is able to tackle ramp constraints, which although very important in practice were rarely considered in previous publications. Most importantly, optimal solutions were obtained for several well-known benchmark instances, including instances of practical relevance, that are not yet known to have been solved to optimality. Computational experiments and their results showed that the method proposed is both simple and extremely effective.

New insights on integer-programming models for the kidney exchange problem

In recent years several countries have set up policies that allow exchange of kidneys between two or more incompatible patient–donor pairs. These policies lead to what is commonly known as kidney exchange programs. The underlying optimization problems can be formulated as integer programming models. Previously proposed models for kidney exchange programs have exponential numbers of constraints or variables, which makes them fairly difficult to solve when the problem size is large. In this work we propose two compact formulations for the problem, explain how these formulations can be adapted to address some problem variants, and provide results on the dominance of some models over others. Finally we present a systematic comparison between our models and two previously proposed ones via thorough computational analysis. Results show that compact formulations have advantages over non-compact ones when the problem size is large.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

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