MCDA applied to performance appraisal of short-haul truck drivers: a case study in a portuguese trucking company

Performance appraisal increasingly assumes a more important role in any organizational environment. In the trucking industry, drivers are the company's image and for this reason it is important to develop and increase their performance and commitment to the company's goals. This paper aims to create a performance appraisal model for trucking drivers, based on a multi-criteria decision aid methodology. The PROMETHEE and MMASSI methodologies were adapted using the criteria used for performance appraisal by the trucking company studied. The appraisal involved all the truck drivers, their supervisors and the company's Managing Director. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used. The results are to be used as a decision-making tool to allocate drivers to the domestic haul service.

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.

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.

MASCEM Restructuring: Ontologies For Scenarios Generation in Power Systems Simulators

The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system’s integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

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 the 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.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

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

Realistic Traffic Scenarios Using a Census Methodology: Vila Real Case Study

This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

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)

O financiamento por capitação ajustada pelo risco em contexto de integração vertical de cuidados de saúde : a utilização dos consumos com medicamentos como proxy da carga de doença em ambulatório

RESUMO - Introdução - Com o presente projecto de investigação pretendeu-se estudar o financiamento por capitação ajustado pelo risco em contexto de integração vertical de cuidados de saúde, recorrendo particularmente a informação sobre o consumo de medicamentos em ambulatório como proxy da carga de doença. No nosso país, factores como a expansão de estruturas de oferta verticalmente integradas, inadequação histórica da sua forma de pagamento e a recente possibilidade de dispor de informação sobre o consumo de medicamentos de ambulatório em bases de dados informatizadas são três fortes motivos para o desenvolvimento de conhecimento associado a esta temática. Metodologia - Este trabalho compreende duas fases principais: i) a adaptação e aplicação de um modelo de consumo de medicamentos que permite estimar a carga de doença em ambulatório (designado de PRx). Nesta fase foi necessário realizar um trabalho de selecção, estruturação e classificação do modelo. A sua aplicação envolveu a utilização de bases de dados informatizadas de consumos com medicamentos nos anos de 2007 e 2008 para a região de Saúde do Alentejo; ii) na segunda fase foram simulados três modelos de financiamento alternativos que foram propostos para financiar as ULS em Portugal. Particularmente foram analisadas as dimensões e variáveis de ajustamento pelo risco (índices de mortalidade, morbilidade e custos per capita), sua ponderação relativa e consequente impacto financeiro. Resultados - Com o desenvolvimento do modelo PRx estima-se que 36% dos residentes na região Alentejo têm pelo menos uma doença crónica, sendo a capacidade de estimação do modelo no que respeita aos consumos de medicamentos na ordem dos 0,45 (R2). Este modelo revelou constituir uma alternativa a fontes de informação tradicionais como são os casos de outros estudos internacionais ou o Inquérito Nacional de Saúde. A consideração dos valores do PRx para efeitos de financiamento per capita introduz alterações face a outros modelos propostos neste âmbito. Após a análise dos montantes de financiamento entre os cenários alternativos, obtendo os modelos 1 e 2 níveis de concordância por percentil mais próximos entre si comparativamente ao modelo 3, seleccionou-se o modelo 1 como o mais adequado para a nossa realidade. Conclusão - A aplicação do modelo PRx numa região de saúde permitiu concluir em função dos resultados alcançados, que já existe a possibilidade de estruturação e operacionalização de um modelo que permite estimar a carga de doença em ambulatório a partir de informação relativa ao seu perfil de consumo de medicamentos dos utentes. A utilização desta informação para efeitos de financiamento de organizações de saúde verticalmente integradas provoca uma variação no seu actual nível de financiamento. Entendendo este estudo como um ponto de partida onde apenas uma parte da presente temática ficará definida, outras questões estruturantes do actual sistema de financiamento não deverão também ser olvidadas neste contexto. ------- ABSTRACT - Introduction - The main goal of this study was the development of a risk adjustment model for financing integrated delivery systems (IDS) in Portugal. The recent improvement of patient records, mainly at primary care level, the historical inadequacy of payment models and the increasing number of IDS were three important factors that drove us to develop new approaches for risk adjustment in our country. Methods - The work was divided in two steps: the development of a pharmacy-based model in Portugal and the proposal of a risk adjustment model for financing IDS. In the first step an expert panel was specially formed to classify more than 33.000 codes included in Portuguese pharmacy national codes into 33 chronic conditions. The study included population of Alentejo Region in Portugal (N=441.550 patients) during 2007 and 2008. Using pharmacy data extracted from three databases: prescription, private pharmacies and hospital ambulatory pharmacies we estimated a regression model including Potential Years of Life Lost, Complexity, Severity and PRx information as dependent variables to assess total cost as the independent variable. This healthcare financing model was compared with other two models proposed for IDS. Results - The more prevalent chronic conditions are cardiovascular (34%), psychiatric disorders (10%) and diabetes (10%). These results are also consistent with the National Health Survey. Apparently the model presents some limitations in identifying patients with rheumatic conditions, since it underestimates prevalence and future drug expenditure. We obtained a R2 value of 0,45, which constitutes a good value comparing with the state of the art. After testing three scenarios we propose a model for financing IDS in Portugal. Conclusion - Drug information is a good alternative to diagnosis in determining morbidity level in a population basis through ambulatory care data. This model offers potential benefits to estimate chronic conditions and future drug costs in the Portuguese healthcare system. This information could be important to resource allocation decision process, especially concerning risk adjustment and healthcare financing.

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.