Optimização de recursos na construção de linhas de caminhos de ferro

Os Caminhos-de-ferro representam um conjunto de abordagens quase ilimitadas, nestes termos, o tema proposto – “A optimização de recursos na construção de linhas de Caminhos de Ferro”, incidirá particularmente sobre a optimização dos recursos: i) materiais; ii) mão-de-obra; iii) equipamentos, afectos a construção da via e da catenária. O presente estudo pretende traçar um encadeamento lógico e intuitivo que permita manter um fio condutor ao longo do todo o seu desenvolvimento, razão pela qual, a sequência dos objectivos apresentados constitui um caminho que permitira abrir sucessivas janelas de conhecimento. O conhecimento da via e da catenária, a compreensão da forma como os trabalhos interagem com os factores externos e a experiência na utilização das ferramentas de planeamento e gestão, são qualidades que conduzem certamente a bons resultados quando nos referimos a necessidade de optimizar os recursos na construção da via e da catenária. A transmissão e reciprocidade da informação, entre as fases de elaboração de propostas e de execução da obra, representam um recurso que pode conduzir a ganhos de produtividade. A coordenação e outro factor determinante na concretização dos objectivos de optimização dos recursos, que se efectua, quer internamente, quer exteriormente. A optimização de recursos na construção da via e da catenária representa o desafio permanente das empresas de construção do sector ferroviário. E neste pressuposto que investem na formação e especialização da sua mão-de-obra e na renovação tecnológica dos seus equipamentos. A optimização dos materiais requer aproximações distintas para o caso da via e para o caso da catenária, assim como, os equipamentos e a mão-de-obra não podem ser desligados, pois não funcionam autonomamente, no entanto a respectiva optimização obedece a pressupostos diferentes.

Addressing the facilities layout design problem through constraint logic programming

One of the most difficult problems that face researchers experimenting with complex systems in real world applications is the Facility Layout Design Problem. It relies with the design and location of production lines, machinery and equipment, inventory storage and shipping facilities. In this work it is intended to address this problem through the use of Constraint Logic Programming (CLP) technology. The use of Genetic Algorithms (GA) as optimisation technique in CLP environment is also an issue addressed. The approach aims the implementation of genetic algorithm operators following the CLP paradigm.

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.

Virtual power producers market strategies

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper detail some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study.

Natural gas system operation: lagrangean optimization techniques

To comply with natural gas demand growth patterns and Europe´s import dependency, the gas industry needs to organize an efficient upstream infrastructure. The best location of Gas Supply Units – GSUs and the alternative transportation mode – by phisical or virtual pipelines, are the key of a successful industry. In this work we study the optimal location of GSUs, as well as determining the most efficient allocation from gas loads to sources, selecting the best transportation mode, observing specific technical restrictions and minimizing system total costs. For the location of GSUs on system we use the P-median problem, for assigning gas demands nodes to source facilities we use the classical transportation problem. The developed model is an optimisation-based approach, based on a Lagrangean heuristic, using Lagrangean relaxation for P-median problems – Simple Lagrangean Heuristic. The solution of this heuristic can be improved by adding a local search procedure - the Lagrangean Reallocation Heuristic. These two heuristics, Simple Lagrangean and Lagrangean Reallocation, were tested on a realistic network - the primary Iberian natural gas network, organized with 65 nodes, connected by physical and virtual pipelines. Computational results are presented for both approaches, showing the location gas sources and allocation loads arrangement, system total costs and gas transportation mode.

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.

Multidimensional aspects related to shiftworkers' health and well-being

The impact of shift and night work on health shows a high inter- and intra-individual variability, both in terms of kind of troubles and temporal occurrence, related to various intervening factors dealing with individual characteristics, lifestyles, work demands, company organisation, family relations and social conditions. The way we define "health" and "well-being" can significantly influence appraisals, outcomes and interventions. As the goal is the optimisation of shiftworkers' health, it is necessary to go beyond the health protection and to act for health promotion. In this perspective, not only people related to medical sciences, but many other actors (ergonomists, psychologists, sociologists, educators, legislators), as well as shiftworkers themselves. Many models have been proposed aimed at describing the intervening variables mediating and/or moderating the effects; they try to define the interactions and the pathways connecting risk factors and outcomes through several human dimensions, which refer to physiology, psychology, pathology, sociology, ergonomics, economics, politics, and ethics. So, different criteria can be used to evaluate shiftworkers' health and well-being, starting from biological rhythms and ending in severe health disorders, passing through psychological strain, job dissatisfaction, family perturbation and social dis-adaptation, both in the short- and long-term. Consequently, it appears rather arbitrary to focus the problem of shiftworkers' health and tolerance only on specific aspects (e.g. individual characteristics), but a systemic approach appears more appropriate, able to match as many variables as possible, and aimed at defining which factors are the most relevant for those specific work and social conditions. This can support a more effective and profitable (for individuals, companies, and society) adoption of preventive and compensative measures, that must refer more to "countervalues" rather than to "counterweights".

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.

Reported missing - learners in SLA/EFL research

Versão integral da revista no link do editor

Azorean Agriculture Efficiency by PAR

Copyright © 2013 Springer Netherlands.

Monitorização integrada para sistemas de alimentação distribuídos

Mestrado em Engenharia Electrotécnica e de Computadores

Dimensionless analysis of constrained damping treatments

One of the most effective ways of controlling vibrations in plate or beam structures is by means of constrained viscoelastic damping treatments. Contrary to the unconstrained configuration, the design of constrained and integrated layer damping treatments is multifaceted because the thickness of the viscoelastic layer acts distinctly on the two main counterparts of the strain energy the volume of viscoelastic material and the shear strain field. In this work, a parametric study is performed exploring the effect that the design parameters, namely the thickness/length ratio, constraining layer thickness, material modulus, natural mode and boundary conditions have on these two counterparts and subsequently, on the treatment efficiency. This paper presents five parametric studies, namely, the thickness/length ratio, the constraining layer thickness, material properties, natural mode and boundary conditions. The results obtained evidence an interesting effect when dealing with very thin viscoelastic layers that contradicts the standard treatment efficiency vs. layer thickness relation; hence, the potential optimisation of constrained and integrated viscoelastic treatments through the use of properly designed thin multilayer configurations is justified. This work presents a dimensionless analysis and provides useful general guidelines for the efficient design of constrained and integrated damping treatments based on single or multi-layer configurations. (C) 2012 Elsevier Ltd. All rights reserved.

Aplicação dos princípios da manutenção Lean na indústria farmacêutica

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

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.