Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

Strategic Planning and Organizational Effectiveness in Social Service Organizations in Portugal

This study aims to understand the reality of social service organizations, the level of implementation of the strategic planning as well as the impact of its application on organizational effectiveness. At first, we will group organizations in clusters according to the level of strategic planning implementation and its degree of effectiveness. Secondly, we will analyse all the different groups. Given the growing number of social service organizations and the consequent complexity of their structures, it turns out the need for these organizations adopt formal management techniques. Strategic planning is a valuable strategic management tool and one of its main objectives is to make organizations more effective. Therefore, the research has been conducted in order to determine if strategic planning is implemented in social service organizations and which effects has its application on organizational effectiveness. The survey, applied to 220 social service organizations, allowed us to gather them into different clusters, showing that different levels of strategic planning determine distinct degrees of organizational efficiency. Finally, it should be noted that findings of this research may be essential to decision makers of these organizations, because it was shown that the adoption of strategic planning has a positive influence on organizational effectiveness of social service organizations.

Plano de negócios para empresa de iluminação de emergência

Dissertação de Mestrado em Finanças Empresariais

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.

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 data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

Planning and implementing a new assessment strategy using an e-learning platform

One of the most difficult issues of e-Learning is the students’ assessment. Being this an outstanding task regarding theoretical topics, it becomes even more challenging when the topics under evaluation are practical. ISCAP’s Information Systems Department is composed of about twenty teachers who have been for several years using an e-learning environment (at the moment Moodle 2.3) combined with traditional assessment. They are now planning and implementing a new e-learning assessment strategy. This effort was undertaken in order to evaluate a practical topic (the use of spreadsheets to solve management problems) common to shared courses of several undergraduate degree programs. The same team group is already experienced in the assessment of theoretical information systems topics using the b-learning platform. Therefore, this project works as an extension to previous experiences being the team aware of the additional difficulties due to the practical nature of the topics. This paper describes this project and presents two cycles of the action research methodology, used to conduct the research. The first cycle goal was to produce a database of questions. When it was implemented in order to be used with a pilot group of students, several problems were identified. Subsequently, the second cycle consisted in solving the identified problems preparing the database and all the players to a broader scope implementation. For each cycle, all the phases, its drawbacks and achievements are described. This paper suits all those who are or are planning to be in the process of shifting their assessment strategy from a traditional to one supported by an e-learning platform.

Knowledge management in higher education institutions: a framework to improve collaboration

The purpose of this paper is to present a framework that increases knowledge sharing and collaboration in Higher Education Institutions. The paper discusses the concept of knowledge management in higher education institutions, presenting a systematization of knowledge practices and tools to linking people (students, teachers, researchers, secretariat staff, external entities)and promoting the knowledge sharing across several key processes and services in a higher education institution, such as: the research processes, learning processes, student and alumni services, administrative services and processes, and strategic planning and management. The framework purposed in this paper aims to improve knowledge practices and processes which facilitate an environment and a culture of knowledge collaboration,sharing and discovery that should characterize an institution of higher education.

Gestão de risco: articulação com o planeamento estratégico e a contabilidade de gestão: estudo aplicado a empresas não financeiras do concelho de Vila do Conde

Este trabalho de investigação tem como objetivo analisar se as empresas do concelho de Vila do Conde, efetuam planeamento estratégico, contabilidade de gestão e/ou gestão de risco. Pretende-se ainda verificar se o facto de existir articulação entre estas três áreas da empresa influencia o resultado do seu EBIT (Earning Before Interest and Taxes). Através da literatura relevante, verificou-se que o planeamento estratégico, a contabilidade de gestão e a gestão de risco têm progredido de modo a responder às mudanças do meio envolvente onde as empresas estão a operar. Contudo, em termos práticos, este estudo permitiu verificar que a implementação da gestão de risco e da contabilidade de gestão no tecido empresarial de Vila do Conde tem sido muito lenta. Os dados foram recolhidos através dum inquérito eletrónico efetuado a 505 empresas do concelho de Vila do Conde de diversas atividades económicas pertencentes aos três grandes sectores (serviços, indústria e comércio). De acordo com os resultados obtidos, concluiu-se que apenas 17% das empresas do concelho de Vila do Conde efetuam simultâneamente planeamento estratégico, contabilidade de gestão e gestão de risco. Conclui-se ainda que as empresas do concelho de Vila do Conde que efetuam em simultâneo planeamento estratégico, contabilidade de gestão e gestão de risco apresentam em média um EBIT (2010) aparenta ser superior às que não fazem planeamento estratégico, contabilidade de gestão e gestão de risco.

Empreitada de construção dos armazéns do polo 2 da plataforma logística do porto de Leixões - Direção e controlo da produção

A Plataforma Logística do Porto de Leixões, administrada pela Administração dos Portos do Douro e Leixões, S.A. (APDL), integra dois Polos situados no concelho de Matosinhos em locais estratégicos para o desenvolvimento das atividades portuária e de logística. É neste contexto que a empresa Luís Simões contactou a APDL no sentido de alugar um espaço para se instalar no Polo 2 da Plataforma Logística do Porto de Leixões. Para que este contrato fosse celebrado existiu um compromisso da APDL de construir dois armazéns com cerca de 10.000m2 cada e ainda um edifício administrativo com cerca de 2.900m2 e todas as redes de infraestruturas, circulações e arranjos exteriores. Após a realização de Concurso Público, a Empreitada de Construção, foi adjudicada à empresa DST - Domingos da Silva Teixeira, S.A.. O presente relatório é referente a um estágio realizado na DST, S.A., em obra, no período de 31 de Janeiro de 2014 e 31 de Julho de 2014. O estágio englobou a direção e controlo da produção das atividades de construção civil que decorreram na empreitada durante este período. O estágio foi efetuado em ambiente real de obra tendo seguido o planeamento habitual de uma empreitada. Foram desenvolvidas numa primeira fase as atividades de preparação e lançamento de consultas de subempreitadas. De seguida foram desenvolvidas tarefas de preparação, controlo de fornecimento, apoio e acompanhamento dos subempreiteiros em obra, destacando-se o acompanhamento dos trabalhos de revestimento exteriores dos edifícios e dos pavimentos de alta planimetria.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

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.

Perspetivas interdisciplinares na planificação didática de Línguas e Culturas Estrangeiras

O presente Relatório Final de Estágio concretiza-se num trabalho de reflexão crítica sobre as práticas realizadas como professora estagiária de Inglês e de Espanhol no âmbito das Unidades Curriculares de Prática Educativa I, II e III, do Curso de Mestrado em Ensino de Inglês e Francês ou Espanhol, da Escola Superior de Educação, do Instituto Politécnico do Porto. A problemática que se apresenta decorre da perspetiva de que o ensino e aprendizagem de uma língua estrangeira baseados na articulação entre disciplinas do currículo contribui para uma aprendizagem holística, desenvolvendo competências que permitem um entendimento geral e global dos fenómenos humanos proporcionando aos alunos a compreensão do saber escolar como um todo articulável, contribuindo para uma leitura lógica e completa da realidade. A sua concretização na sala de aula impõe que o professor planifique atividades e opte por estratégias de ensino e de aprendizagem diversificadas, onde convergem finalidades, conceitos e metodologias de diversas disciplinas do currículo. Assim, este trabalho descreve a planificação de práticas de ensino e aprendizagem de Inglês e Espanhol em articulação com os conteúdos e os objetivos da disciplina de Estudo do Meio no Primeiro Ciclo. A planificação de práticas de Inglês no Segundo Ciclo foi realizada em articulação com a disciplina de História e Geografia de Portugal, com base na metodologia de projeto. As opções adotadas na planificação didática são consideradas estratégicas para uma aprendizagem significativa. Palavras-chave: perspetiva interdisciplinar do ensino; planificação didática de línguas estrangeiras; compreensão integrada do saber escolar; aprendizagem por projeto