Group decision making for selection of an information system in a business context

The main objective of this work is to report on the development of a multi-criteria methodology to support the assessment and selection of an Information System (IS) framework in a business context. The objective is to select a technological partner that provides the engine to be the basis for the development of a customized application for shrinkage reduction on the supply chains management. Furthermore, the proposed methodology di ers from most of the ones previously proposed in the sense that 1) it provides the decision makers with a set of pre-defined criteria along with their description and suggestions on how to measure them and 2)it uses a continuous scale with two reference levels and thus no normalization of the valuations is required. The methodology here proposed is has been designed to be easy to understand and use, without a specific support of a decision making analyst.

Georeferencing Road Accidents with Google Earth: Transforming Information into Knowledge for Decision Support

Over the last fifty years mobility practices have changed dramatically, improving the way travel takes place, the time it takes but also on matters like road safety and prevention. High mortality caused by high accident levels has reached untenable levels. But the research into road mortality stayed limited to comparative statistical exercises which go no further than defining accident types. In terms of sharing information and mapping accidents, little progress has been mad, aside from the normal publication of figures, either through simplistic tables or web pages. With considerable technological advances on geographical information technologies, research and development stayed rather static with only a few good examples on dynamic mapping. The use of Global Positioning System (GPS) devices as normal equipments on automobile industry resulted in a more dynamic mobility patterns but also with higher degrees of uncertainty on road traffic. This paper describes a road accident georeferencing project for the Lisbon District involving fatalities and serious injuries during 2007. In the initial phase, individual information summaries were compiled giving information on accidents and its majour characteristics, collected by the security forces: the Public Safety Police Force (Polícia de Segurança Pública - PSP) and the National Guard (Guarda Nacional Republicana - GNR). The Google Earth platform was used to georeference the information in order to inform the public and the authorities of the accident locations, the nature of the location, and the causes and consequences of the accidents. This paper also gives future insights about augmented reality technologies, considered crucial to advances to road safety and prevention studies. At the end, this exercise could be considered a success because of numerous consequences, as for stakeholders who decide what to do but also for the public awareness to the problem of road mortality.

Intestinal spirochetosis: first cases reported in Brazil and the use of immunohistochemistry as an aid in histopathological diagnosis

Colonization of the colon and rectum by intestinal spirochetes is detected for the first time in Brazil in 4 of 282 (1.41%) patients who had undergone sigmoidoscopy and/or colonoscopy with a histopathological diagnosis of chronic non specific-colitis. This frequency is probably understimated, since surgically obtained specimens were not considered in the present study. Histopathological diagnosis was performed using routine stains like hematoxylin-eosin which showed the typical, of 3-µm thick hematoxyphilic fringe on the brush border of the surface epithelium, and by silver stains like the Warthin-Starry stain. Immunohistochemical procedures using two, polyclonal, primary antibodies, one against Treponema pallidum and the other against Leptospira interrogans serovar copenhageni serogroup Icterohaemorrhagiae cross-reacted with spirochetal antigen/s producing a marked contrast of the fringe over the colonic epithelium, preserving the spiral-shaped morphology of the parasite. In one case with marked diarrhea, immunohistochemistry detected spirochetal antigen/s within a cell in an intestinal crypt, thus demonstrating that the infection can be more widely disseminated than suspected using routine stains. Immunohistochemical procedures, thus, greatly facilitate the histological diagnosis of intestinal spirochetosis and may contribute to a better understanding of the pathogenesis of the disease. Transmission and scanning electron microscopy performed in one case showed that the spirochete closely resembled the species designated as Brachyspira aalborgi.

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

DC Fuzzy Multicriteria Approach to Increase the Probability of Delivering Power in Distribution Networks

This paper proposes a methodology to increase the probability of delivering power to any load point through the identification of new investments. The methodology uses a fuzzy set approach to model the uncertainty of outage parameters, load and generation. A DC fuzzy multicriteria optimization model considering the Pareto front and based on mixed integer non-linear optimization programming is developed in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power to all customers in the distribution network at the minimum possible cost for the system operator, while minimizing the non supplied energy cost. To illustrate the application of the proposed methodology, the paper includes a case study which considers an 33 bus distribution network.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Intelligent decision support systems for collaboration in industrial plants

Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

The Vasco da Gama Bridge on the Tagus Estuary: A paradigm of bad decision making, but good post-evaluation

World Transport Policy & Practice, Vol.6, nº2, (2000)

How composite indicators of innovation can influence technology policy decision?

Based on the report for Project III of the PhD programme on Technology Assessment and prepared for the Winter School that took place at Universidade Nova de Lisboa, Caparica Campus on the 6th and 7th of December 2010.

Modelo de seleção de fornecedores baseado em dados reais – desenvolvimento de ferramentas para os métodos de seleção

A seleção de fornecedores é considerada atualmente estratégica para as empresas que estão inseridas em ambientes cada vez mais dinâmicos e exigentes. Nesta dissertação são determinados os critérios e métodos mais usados no problema de seleção de fornecedores. Para estes serem alcançados, analisaram-se artigos da área e de ilustres autores para assim se perceber quais os critérios das áreas mais influentes, na hora de tomada de decisão sobre os melhores fornecedores para as empresas. A partir deste estudo foi construído um inquérito de resposta curta, enviado a empresas a laborar em Portugal, para se obter as importâncias dadas aos critérios por parte das empresas. Com estas respostas conclui-se que critérios relacionados com a qualidade e o custo são os mais relevantes. Relativamente aos métodos, foram estudados teórica e praticamente, o AHP e o SMART. O primeiro por ser o mais referenciado nos artigos estudados e o segundo por ser o mais simples de implementar e usar. No SMART foram criadas as funções valor para regerem o funcionamento do método. Estas funções foram desenvolvidas de raiz, com base num estudo bibliográfico prévio para cada um dos subcritérios, para se entender qual o melhor tipo de função a aplicar definindo matematicamente melhor o comportamento de cada um deles. A tomada de decisão é bastante importante nas organizações, pois pode conduzir ao sucesso ou insucesso. Assim é explicado a envolvente da tomada de decisão, o problema da seleção dos fornecedores, como se desenvolve o processo de seleção e quais são os métodos existentes para auxiliar a escolha dos mesmos. Por fim é apresentado o modelo proposto baseado nos resultados obtidos através do inquérito, e a aplicação dos dois métodos (AHP e SMART) para um melhor entendimento dos mesmos.

Os sistemas de apoio à decisão na área dos cuidados primários: implementação de um sistema de apoio à decisão numa unidade de saúde familiar

Saber qual o papel de um Sistema de Apoio à Decisão na gestão estratégica de uma Unidade de Saúde Familiar; perceber qual a importância, no desempenho deste tipo de instituições, que estes Sistemas de Informação poderão assumir e identificar de que forma este gênero de software pode auxiliar a tomada de decisões estratégica da gestão das Unidades de Cuidados de Saúde Primários, são algumas das interrogações cuja relevância se verifica ser cada vez mais crescente e que se irão analisar no presente estudo. Para dar resposta às interrogações supra citadas é necessário conhecer o contexto no qual a organização está inserida, assim como perceber se a visão dos seus colaboradores (realizando-se para isso um inquérito por questionário aos colaboradores da Unidade de Saúde Familiar) é idêntica à realidade demonstrada através dos dados do histórico da instituição (recolhendo, estudando e efetuando estudos analíticos com o auxílio de um Sistema de Apoio à Decisão escolhido para o efeito – Weka). Tendo em conta o percurso anteriormente referido é assim possível inferir que é notória a positividade que os Sistemas de Apoio à Decisão podem ter no que é o dia-a-dia de uma Unidade de Saúde Familiar, tendo em conta que facilitam a análise de dados e podem até antecipar cenários futuros analisando o passado da instituição.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Decision making processes based on innovation indicators: which implications for technology assessment?

Based on the report for “Project IV” unit of the PhD programme on Technology Assessment. This thesis research has the supervision of António Moniz (FCT-UNL and ITAS-KIT) and Manuel Laranja (ISEG-UTL). Other members of the thesis committee are Stefan Kuhlmann (Twente University), Leonhard Hennen (Karlsruhe Institute of Technology-ITAS), Tiago Santos Pereira (Universidade de Coimbra/CES) and Cristina Sousa (FCT-UNL).